Traceability error indication after AI / ML data collection and reporting

Abstract

The invention relates to traceability error indication after AI / ML data collection and reporting. The disclosed method includes receiving, from a network entity, data provided by the network entity, the data including data collected for an operation, where the operation includes an artificial intelligence or machine learning operation; receiving, from the network entity, traceability error information associated with the data that has been provided; and performing an action based on traceability error information received from the network entity associated with the data that has been provided.

Description

Technical Field

[0001] The exemplary and non-limiting example embodiments generally relate to communication, and more specifically to retrospective error indication following AI / ML data collection and reporting. Background Technology

[0002] Communication devices are known to gain access to a communication network by accessing network nodes. Summary of the Invention

[0003] According to one aspect, an apparatus includes: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to at least: collect data for operation, wherein the operation includes artificial intelligence or machine learning operations; report the data collected for the operation to a network entity; determine traceability error information associated with the reported data; and send the traceability error information associated with the reported data to the network entity.

[0004] According to one aspect, an apparatus includes: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to at least: configure a user equipment to collect data for operation, wherein the operation includes artificial intelligence or machine learning operations; receive a report from the user equipment including data collected for operation; receive traceability error information associated with reported data from the user equipment; and perform an action based on the traceability error information received from the user equipment and associated with the reported data.

[0005] According to one aspect, an apparatus includes: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to at least: receive a report from a first user equipment, the report including data collected for operation, wherein the operation includes artificial intelligence or machine learning operations; send the reported, collected data for operation to a second user equipment; receive from the second user equipment a trigger message for performing a traceability data error check on the reported data to determine traceability error information; and send the trigger message to the first user equipment for performing a traceability data error check on the reported data to determine traceability error information.

[0006] According to one aspect, an apparatus includes: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to at least: receive data provided by a network entity, the data including data collected for operation, wherein the operation includes artificial intelligence or machine learning operations; receive traceability error information associated with the provided data from the network entity; and perform an action based on the traceability error information associated with the provided data received from the network entity.

[0007] According to one aspect, an apparatus includes: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to at least: collect data for operation, wherein the operation includes artificial intelligence or machine learning operations; provide the collected data for operation to a user equipment; determine traceability error information associated with the provided data; and send the traceability error information associated with the provided data to the user equipment. Attached Figure Description

[0008] The foregoing aspects and other features are explained in the following description in conjunction with the accompanying drawings.

[0009] Figure 1 This is a block diagram of one possible, non-limiting system in which exemplary embodiments can be practiced.

[0010] Figure 2 This is a diagram of an example embodiment (Example A) of the scheme described herein.

[0011] Figure 3 This is an illustration of an example embodiment (Example B) of the scheme described herein.

[0012] Figure 4 It is an example device configured to implement the examples described herein.

[0013] Figure 5 A representation of an example of a non-volatile memory medium used to store instructions implementing the examples described herein is shown.

[0014] Figure 6 This is an example method based on the examples described in this article.

[0015] Figure 7 This is an example method based on the examples described in this article.

[0016] Figure 8 This is an example method based on the examples described in this article.

[0017] Figure 9 This is an example method based on the examples described in this article.

[0018] Figure 10 This is an example method based on the examples described in this article. Detailed Implementation Go to Figure 1 The figure illustrates a block diagram of one possible and non-limiting example of which can be practiced. It shows a user equipment (UE) 110, a radio access network (RAN) node 170, and (multiple) network elements 190. Figure 1 In the example, User Equipment (UE) 110 wirelessly communicates with Wireless Network 100. The UE is a wireless device that can access Wireless Network 100. UE 110 includes one or more processors 120, one or more memories 125, and one or more transceivers 130 interconnected via one or more buses 127. Each of the one or more transceivers 130 includes a receiver Rx 132 and a transmitter Tx 133. The one or more buses 127 may be address, data, or control buses and may include any interconnecting mechanism, such as a series of lines on a motherboard or integrated circuit, fiber optic cables, or other optical communication devices. The one or more transceivers 130 are connected to one or more antennas 128. The one or more memories 125 include computer program code 123. UE 110 includes modules comprising one or both of portions 140-1 and / or 140-2, which can be implemented in various ways. The module may be implemented in hardware as module 140-1, such as being implemented as part of one or more processors 120. Module 140-1 may also be implemented as an integrated circuit or via other hardware such as a programmable gate array. In another example, module 140 may be implemented as module 140-2, which is implemented as computer program code 123 and executed by one or more processors 120. For example, one or more memories 125 and computer program code 123 may be configured to enable user equipment 110 to perform one or more operations as described herein using one or more processors 120. UE 110 communicates with RAN node 170 via radio link 111. In this example, RAN node 170 is a base station that provides access to wireless network 100 for wireless devices such as UE 110. RAN node 170 can be a base station for, for example, 5G (also known as New Radio (NR)). In 5G, RAN node 170 can be an NG-RAN node, which is defined as a gNB or ng-eNB. A gNB is a node that provides NR user plane and control plane protocol termination to the UE and is connected to the 5GC (e.g., multiple network elements 190) via an NG interface (such as connection 131). An ng-eNB is a node that provides E-UTRA user plane and control plane protocol termination to the UE and is connected to the 5GC via an NG interface (such as connection 131). An NG-RAN node can include multiple gNBs, which can also include a central unit (CU) (gNB-CU) 196 and multiple distributed units (DUs) (gNB-DU) (where DU 195 is shown). Note that DU 195 can include or be coupled to and control a radio unit (RU). gNB-CU 196 is a logical node that hosts the Radio Resource Control (RRC), SDAP, and PDCP protocols of the gNB or the RRC and PDCP protocols of the en-gNB, controlling the operation of one or more gNB-DUs. gNB-CU 196 terminates the F1 interface connected to gNB-DU 195. The F1 interface is illustrated as reference numeral 198, but reference numeral 198 also illustrates a link between remote elements of RAN node 170 and centralized elements of RAN node 170, such as between gNB-CU and gNB-DU. gNB-DU 195 is a logical node that hosts the RLC, MAC, and PHY layers of the gNB or en-gNB, and its operation is partially controlled by gNB-CU 196. A gNB-CU 196 supports one or more cells. A cell can be supported using one gNB-DU 195, or a cell can be supported / shared using multiple DUs under RAN sharing. The gNB-DU 195 terminates the F1 interface 198 connected to the gNB-CU 196. Note that the DU 195 is considered to include the transceiver 160, for example, as part of the RU; however, some examples allow the transceiver 160 to be part of a separate RU, for example, under the control of and connected to the DU 195. The RAN node 170 can also be an eNB (evolved Node B) base station for LTE (Long Term Evolution), or any other suitable base station or node. RAN node 170 includes one or more processors 152, one or more memories 155, one or more network interfaces (N / WI / F) 161, and one or more transceivers 160 interconnected via one or more buses 157. Each of the one or more transceivers 160 includes a receiver Rx 162 and a transmitter Tx 163. The one or more transceivers 160 are connected to one or more antennas 158. The one or more memories 155 include computer program code 153. CU 196 may include processor(s) 152, memories 155, and network interfaces 161. Note that DU 195 may also contain its own one or more memories and processor(s) and / or other hardware, but these are not shown. RAN node 170 includes module 150, which comprises one or both of portions 150-1 and / or portions 150-2. Module 150 can be implemented in various ways. Module 150 can be implemented in hardware as module 150-1, for example, as a portion of one or more processors 152. Module 150-1 can also be implemented as an integrated circuit or via other hardware, such as a programmable gate array. In another example, module 150 can be implemented as module 150-2, which is implemented as computer program code 153 and executed by one or more processors 152. For example, one or more memories 155 and computer program code 153 are configured to enable RAN node 170 to perform one or more operations as described herein using one or more processors 152. Note that the functionality of module 150 can be distributed, such as distributed between DU 195 and CU 196, or implemented only in DU 195. One or more network interfaces 161 communicate over the network, such as via links 176 and 131. Two or more gNBs 170 may communicate using, for example, link 176. Link 176 may be wired or wireless or both, and may implement, for example, an Xn interface for 5G, an X2 interface for LTE, or other suitable interfaces for other standards. One or more buses 157 may be address, data, or control buses and may include any interconnecting mechanism, such as a series of lines on a motherboard or integrated circuit, optical fiber or other optical communication equipment, wireless channels, etc. For example, one or more transceivers 160 may be implemented as a Remote Radio Header (RRH) 195 for LTE or a Distributed Unit (DU) 195 for a gNB implementation of 5G, wherein other elements of the RAN node 170 may be physically located differently from the RRH / DU 195, and one or more buses 157 may be partially implemented as, for example, fiber optic cables or other suitable network connections to connect other elements of the RAN node 170 (e.g., Central Unit (CU), gNB-CU 196) to the RRH / DU 195. Reference numeral 198 also indicates those suitable network links(s). A RAN node / gNB may include one or more TRPs, and the methods described herein can be applied to these TRPs. Figure 1 The diagram shows RAN node 170 including TRP 51 and TRP 52 in addition to the TRP represented by transceiver 160. Similar to transceiver 160, TRP 51 and TRP 52 may each include a transmitter and a receiver. RAN node 170 may carry or include... Figure 1 Other TRPs not shown. In NR, the forwarding node is called the Integrated Access and Backhaul (IAB) node. The mobile terminal portion of the IAB node facilitates backhaul (parent link) connections. In other words, the mobile terminal portion includes the functions that carry the UE. The distributed unit portion of the IAB node facilitates so-called access link (sub-link) connections (i.e., providing backhaul for access link UEs, and for other IAB nodes in the case of multi-hop IABs). In other words, the distributed unit portion is responsible for certain base station functions. IAB scenarios can follow a so-called split architecture, where the central unit carries higher-level protocols for the UE and terminates at the control plane and user plane interfaces of the 5G core network. Note that the description in this document indicates that a "cell" performs a function; however, it should be clear that the equipment forming a cell can perform functions. A cell constitutes part of a base station. That is, each base station can correspond to multiple cells. For example, for a single carrier frequency and associated bandwidth, there can be three cells, each covering one-third of a 360-degree area, such that the coverage area of ​​a single base station is approximately elliptical or circular. Furthermore, each cell can correspond to a single carrier, and a base station can use multiple carriers. Therefore, if each carrier corresponds to three 120-degree cells and there are two carriers, the base station has a total of six cells. Wireless network 100 may include one or more network elements 190, which may include core network functions and provide connectivity to other networks (e.g., telephone networks and / or data communication networks (e.g., the Internet)) via one or more links 181. Such core network functions for 5G may include (multiple) Location Management Functions (LMF) and / or (multiple) Access and Mobility Management Functions (AMF) and / or (multiple) User Plane Functions (UPF) and / or (multiple) Session Management Functions (SMF). Such core network functions for LTE may include MME (Mobility Management Entity) / SGW (Serving Gateway) functions. Such core network functions may include SON (Self-Organizing / Optimizing Network) functions. These are merely example functions that may be supported by (multiple) network elements 190, and note that both 5G and LTE functions may be supported. RAN node 170 is coupled to network element 190 via link 131. Link 131 may be implemented as, for example, an NG interface for 5G, or an S1 interface for LTE, or other suitable interfaces for other standards. Network element 190 includes one or more processors 175 interconnected via one or more buses 185, one or more memories 171, and one or more network interfaces (N / WI / F) 180. The one or more memories 171 include computer program code 173. The computer program code 173 may include SON and / or MRO functions 172. Wireless network 100 can implement network virtualization, which is the process of combining hardware and software network resources and network functions into a single software-based management entity, or virtual network. Network virtualization involves platform virtualization, often combined with resource virtualization. Network virtualization is categorized as either external, combining many networks or parts of networks into virtual units, or internal, providing network-like functionality to software containers on a single system. Note that the virtualized entities created by network virtualization are still implemented at some level using hardware such as processors 152 or 175 and memories 155 and 171, and these virtualized entities also produce technical effects. Computer-readable storage devices 125, 155, and 171 can be of any type suitable for the local technical environment and can be implemented using any suitable data storage technology, such as semiconductor-based memory devices, flash memory, magnetic storage devices and systems, optical storage devices and systems, non-transitory memory, transient memory, fixed memory, and removable memory. Computer-readable storage devices 125, 155, and 171 can be components for performing storage functions. As a non-limiting example, processors 120, 152, and 175 can be of any type suitable for the local technical environment and can include one or more of a general-purpose computer, a special-purpose computer, a microprocessor, a digital signal processor (DSP), and a processor based on a multi-core processor architecture. Processors 120, 152, and 175 can be components for performing functions such as controlling UE 110, RAN node 170, network element(s) 190, and other functions as described herein. Typically, various example embodiments of user equipment 110 may include, but are not limited to, cellular phones (such as smartphones), tablet computers, personal digital assistants (PDAs) with wireless communication capabilities, portable computers with wireless communication capabilities, image capture devices (such as digital cameras with wireless communication capabilities), gaming devices with wireless communication capabilities, music storage and playback devices with wireless communication capabilities, internet devices that allow wireless internet access and browsing, tablet computers with wireless communication capabilities, and portable units or terminals combining such functions. UE 110 may also be a vehicle (such as a car) or a UE installed in a vehicle, a UAV (such as a drone), or a UE installed in a UAV. User equipment 110 may be a terminal device, such as a mobile phone, mobile device, sensor device, etc., which may be a device used by a user or not. UE 110, RAN node 170, and / or (multiple) network elements 190 (and associated memory, computer program code, and modules) can be configured to implement (e.g., partially implement) the methods described herein. Therefore, UE 110's Figure 1 The computer program code 123, module 140-1, module 140-2, and other elements / features shown herein can implement the user equipment-related aspects of the examples described herein. Similarly, RAN node 170's... Figure 1 The computer program code 153, module 150-1, module 150-2, and other elements / features shown herein can implement the gNB / TRP-related aspects of the examples described herein. Figure 1 The computer program code 173 shown, along with other elements / features, can be configured to implement the network element-related aspects of the examples described herein. This introduces a suitable, but not limiting, technical background for practicing these exemplary embodiments, which will now be described in more detail. To date, the application of AI / ML in wireless communication has been limited to implementation-based approaches, both on the network and UE sides. However, leveraging features that enable support for improvements in AI / ML-based algorithms to enhance the air interface could potentially lead to enhanced performance, such as improved throughput, robustness, accuracy, or reliability (depending on the use case), and could also reduce complexity / overhead. Therefore, 3GPP is currently actively promoting AI / ML for the air interface. Use cases for AI / ML technology applied to the NR air interface include CSI feedback enhancement, beam management, and positioning accuracy enhancement. CSI feedback enhancement use cases include spatial-frequency CSI compression using a two-way AI model, or temporal CSI prediction using a UE-side model. Beam management use cases include spatial downlink beam prediction for beam set A based on measurements from beam set B, and temporal downlink beam prediction for beam set A based on historical measurements from beam set B. Positioning accuracy enhancement use cases include direct AI / ML positioning and AI / ML-assisted positioning. Direct AI / ML positioning use cases include AI / ML model outputs of the UE location, such as fingerprinting based on channel observations as input to the AI / ML model. AI / ML-assisted positioning use cases include new measurements and / or enhanced AI / ML model outputs of existing measurements, such as LOS / NLOS identifiers, measurement time and / or angle, and measurement probability. The general AI / ML framework used for single-end AI / ML models can be applied to the examples described herein. The functionality described and used herein refers to AI / ML features / feature groups (FGs) enabled through configuration(s), where configuration(s) are supported based on conditions indicated by UE capabilities. AI / ML data collection is the process by which network nodes, management entities, or user-defined users (UEs) collect data for the purposes of AI / ML model training, data analysis, and inference. Data collection is a function that provides input data for model training, management, and inference functions. Training data is the data required as input for AI / ML model training. Monitoring data is the data required as input for the management of AI / ML models or AI / ML functions, and inference data is the data required as input for AI / ML inference functions. Information with potential impact regarding AI / ML-based location data collection may include (1-9): 1) Truth labels, including reports from entities generated from the label data. 2) Measurement (corresponding to model input), including reports generated from measurement data to entities. 3) Quality indicators used for truth labels and / or measurements and / or associated with truth labels and / or measurements, or from reports from entities that generate label and / or measurement data, and / or as requests from different entities (e.g., data collection, etc.). 4) Multiple RS configurations, at least for deriving measurements, or as a request from the data generating entity (UE / PRU / TRP) to the LMF, and / or as auxiliary signaling from the LMF to the UE / PRU / TRP (for location measurements, there may be no enhancements or new RS configurations on top of the multiple existing RS configurations). 5) Timestamps, at least for the collected data and / or associated with the collected data, when the measurement and truth labels are generated by different entities, the timestamps for the measurement and truth labels are separate, or the reports from the data generating entity are together with the collected data, and / or as LMF auxiliary signaling (for localization measurements, there may be no enhancements above the timestamps in existing localization measurement reports, or no new timestamp reports, and whether and how the above information can be applied to different aspects of AI / ML LCM (e.g., training, updating, monitoring, etc.) may vary). From the perspective of RAN1, transmitting data from the entity that generates the data to different entities is not excluded. If any canonical effects are identified, these effects can differ between different location use cases. The necessity of other information for data collection (e.g., scenario identifiers, LOS / NLOS status, timing errors, etc.) can be case-specific. 6) Details of requests / reports for labels and / or other training data, and enabling the transfer of collected labels and / or other training data to the training entity when the training entity is not the same entity that obtained the labels and / or other training data. 7) Instructing (multiple) reference signal configurations to derive auxiliary signaling for labels and / or other training data. 8) Request / report of training data: truth labels; measurements corresponding to model inputs; truth labels and / or associated information corresponding to the measurements corresponding to model inputs. 9) Auxiliary signaling and procedures to facilitate the generation of training data: (multiple) reference signals (e.g., PRS / SRS) configuration and configuration identifiers; auxiliary information, such as between LMF and UE / PRU, for label calculation / generation, and label validity / quality conditions, etc. (Whether such auxiliary signaling and procedures can be applied to (multiple) other aspects of AI / ML LCM may be case-specific). Where applicable, there may be different entities generating training data, as well as different types of training data. Where applicable, consider the following two scenarios: the training entity is the same as the entity generating the training data, and the training entity is different from the entity generating the training data. For location use cases, the selection, (de)activation, switching, and rollback of models or functions can be initiated by the UE, gNB, or LMF. In this regard, different situations can be distinguished between models applicable to the UE side and models applicable to the network side. Various scenarios arise when the data-generating entity and the termination entity differ in data collection, model delivery / transfer, and function-to-entity mapping analysis. For example, in model training, for a UE-side model, training data can be generated by the UE, while the termination point for the training data can include the UE or a UE-side OTT server. OAM or the core network can be used for UE-side model training. LMF can also be used for UE-side model training. In one scenario, for training data generation for AI / ML-based positioning, measurements and related data (e.g., timestamps) are generated by the PRU and / or non-PRU UE. In another scenario, for training data generation for AI / ML-based positioning, channel measurements and related data (e.g., timestamps) are generated by the PRU and / or non-PRU UE. For training data collection for AI / ML-based positioning, the collected data samples may include the following components: Part A, which includes channel measurements, quality indicators for channel measurements, and timestamps for channel measurements; and Part B, which includes truth labels (or their approximations), quality indicators for labels, and timestamps for labels. If a training data sample contains both Part A and Part B, it can be assumed that Part A and Part B in a training data sample are for the same UE (PRU or non-PRU UE) and for the same location associated with Part B. For training data generation for AI / ML-based localization, labels and their associated data (e.g., timestamps) can be generated from: PRU, non-PRU UE with the estimated location, or LMF. In the example, for (multiple) UE-side models developed (e.g., trained, updated) on the UE side, for data collection, the NW sends (multiple) data collection-related configurations and their associated IDs (associated IDs for each sub-use case related to the NW-side additional conditions) via signaling. (Multiple) UEs collect data corresponding to (multiple) associated IDs, and the AI / ML model is developed (e.g., trained, updated) on the UE side based on the collected data corresponding to (multiple) associated IDs. gNB-centric and OAM-centric approaches for NW-side data collection can be considered, as these approaches are relevant to beam management use cases. The data collection process from the UE to the NW (e.g., gNB, LMF, or OAM) can be considered for NW-side model LCM, including training, inference, and management. RRC configuration can be used to configure radio measurements and related reports to enable data collection for NW-side training. Therefore, various aspects of data collection for AI / ML can be implemented in wireless communication networks, such as data collection processes from the UE to the NW (e.g., gNB, LMF, or OAM) for NW-side model LCM (including training, inference, and management). The example described in this paper relates to retrospective error reporting for AI / ML data collection. Consider the following scenario where a network configures a UE for data collection for AI / ML operations (e.g., training an AI / ML model). In this case, the UE collecting data generates a dataset consisting of measurements and / or estimates performed at the UE, along with any associated ground truth labels. For example, for a positioning use case (e.g., fingerprint-based direct UE positioning), a UE with a known location (using a 3GPP-based or non-3GPP-based positioning method) can measure the channel impulse response (CIR) to create a dataset of CIR pairs with associated locations (e.g., estimated locations using some positioning technique). The UE then reports the collected data to the network for use in AI / ML operations, such as AI / ML model training. Note that the AI / ML operation can be performed at the network or at another UE. When the operation is performed at another UE, the network can provide the collected data to that other UE. However, any errors that may exist in the collected data can adversely affect AI / ML operations. For example, in fingerprint-based direct localization use cases, the performance of an AI / ML model trained on such labeled input data can be negatively impacted when the labels (e.g., location) associated with the input data (e.g., CIR) are inaccurate. Therefore, ensuring data quality (e.g., accurate labeling and / or discarding (multiple) erroneous data samples) before using a dataset for intended AI / ML operations is essential to ensure high performance. Furthermore, even when a dataset with erroneous data samples has already been used, recognizing the errors can still allow for, for example, retraining or updating the model to avoid poor model performance. When reporting data, the presence of errors in the collected data may not be known at the UE. Therefore, the UE cannot filter or correct erroneous data samples before reporting the collected data. However, there is currently no mechanism for the UE to indicate any errors related to the dataset it has already reported to the network. The examples described herein relate to data collection for AI / ML. Specifically, the examples described herein involve a UE retroactively instructing the network on error information for reported data. This retroactive error information is sent when / if the UE identifies an error in the reported data. The retroactive error identification at the UE may be triggered by the network or may be performed autonomously by the UE. The retroactive error information may include one or more of the following: a unique identifier of a (potentially) erroneous data sample, or a unique identifier of a set of data samples including the (potentially) erroneous data sample, (multiple) potentially erroneous components of (multiple) data samples, correction of (multiple) erroneous components of (multiple) data samples, and / or the level of error in (multiple) erroneous components of (multiple) data samples. The network receives retrospective error information from the UE regarding data already collected from the UE. If the collected data has not yet been used for AI / ML operations (e.g., model training), the network can correct data samples based on the received retrospective error information regarding the collected data (e.g., by applying corrections to labels and / or discarding (multiple) erroneous data samples). If the dataset with erroneous data samples has already been used, for example, to train a model, the network can utilize the error reports to perform, for example, model retraining or model updates to avoid poor model performance (if the identified errors are believed to affect model performance). When reporting data, the UE may not be aware of the existence of errors in the collected data. Therefore, the UE cannot filter or correct erroneous data samples before reporting the collected data. However, at a later time, the UE may become aware of the existence of errors in one or more of the collected data samples within the reported dataset. Several example scenarios (A–D) are listed below, in which the UE may only become aware of errors in the data samples after the data has already been reported. A) When UE sensors are used for data generation, for example, in a location use case, if the training data labels for the UE location are determined using a non-3GPP-based solution, the UE may determine that the labels (locations) in the training data collected over a certain period may be incorrect after recalibrating the sensors at the UE. Furthermore, based on the recalibration, the UE can determine error corrections for the labels. B) Similarly, for GNSS or TBS positioning, the UE may not obtain the necessary correction data (e.g., GNSS differential correction as part of auxiliary data in LPP) long after performing positioning estimation using these positioning technologies. C) The UE may recognize that the reference time it used to timestamp its measurements is / was incorrect, for example, after selecting a different synchronization source. The UE may also recognize the time offset that caused the error and determine the value to compensate for the incorrect timestamps it previously provided in the dataset. D) The UE may become aware that its algorithms and / or AI / ML models used to perform measurements and / or estimations (e.g., using AI / ML inference) contain, for example, systematic errors, defects, etc. This awareness may occur, for example, after the UE (e.g., based on AI / ML performance monitoring) receives feedback from the network about its estimations and after internally debugging / checking its algorithms. It is important to note that this awareness may occur at the application layer or on any external server (e.g., the UE's vendor's server). By instructing the network to retrospectively identify errors in the reported data, the UE can create an awareness of errors in the collected data (from the UE) at the network level. This allows the network to correct data samples (e.g., by applying corrections to labels) and / or discard (multiple) erroneous data samples before using the dataset for an expected AI / ML operation, ensuring high performance for that operation. Furthermore, if a dataset with erroneous data samples has already been used, the network can perform, for example, model retraining or model updates to avoid poor model performance (if the identified errors are believed to negatively impact model performance). Figure 2 An embodiment of the scheme described herein is illustrated, demonstrating signaling exchange between UE-A 110-A and a network (e.g., RAN node 170 or one or more network elements 190). The steps are as follows: Step 0: The NW determines the configuration required for data collection and provides that configuration to the UE, such as the configuration of reference signals to be transmitted (e.g., DL PRS), and the configuration of measurements and reporting (e.g., location-related RSRP measurements to be performed by the UE, their reporting frequencies, etc.). The NW also transmits any reference signals required for data collection, such as DL PRS, via the TRP. Step 1: The UE collects data according to the network configuration. Step 2: After completing data collection, the UE reports the collected data, for example, based on the report configuration it receives from the NW. The UE adds a unique identifier to each data sample in the dataset. This unique identifier allows the UE and the network to pinpoint the data sample for which the UE has provided traceable error information (see Steps 6 and 7). Step 3: The NW uses the reported dataset for AI / ML operations, such as (re)training or monitoring AI / ML models on the LMF or gNB side, or storing the dataset for later use in AI / ML operations. The network may also forward the data to other UEs or network entities for AI / ML operations. After step 3, step 4 (or step 5) may occur immediately or later. In other words, step 3 is not necessarily followed immediately by step 4 or step 5. Step 4 (optional): The network triggers a retrospective data sample check on the reported data by the UE. This step is performed by the network only if the network suspects that the collected data sample is erroneous or corrupted. In one example, the network determines what is needed based on monitoring the performance of a pre-trained AI / ML model using a provided dataset. For instance, by employing a previously trained high-performance AI / ML model, measurements from the dataset can be used as input for inference, and the inference output can then be compared to labels in the dataset to verify whether the labels are close to the expected truth. In another example, the network can determine the need for error checking by comparing the dataset with other similar datasets, such as those collected by other UEs or network entities and involving the same area, having similar timestamps, etc. In another example, the network can determine the need for retrospective error checks by observing the consistency between the collected data and previously collected data from the same device and identifying potential outliers. For example, the network can evaluate the probability that each provided dataset (e.g., a set of collected data, such as 20KB) of a configured size is an anomalous dataset. An anomalous dataset could be, for example, a dataset that includes at least one value significantly exceeding those provided by the device to date; or a dataset that does not conform to the trend of the most recent n provided datasets (n being an integer). The trigger message may include: a unique identifier of a data sample that the network considers potentially erroneous, or a unique identifier of a set of data samples that includes the potentially erroneous data sample, or a set of data samples that the network considers potentially erroneous, wherein the data samples may consist of a measurement and / or truth label (or an approximation thereof) and an associated quality indicator and an associated timestamp. Step 5: The UE performs a traceability data sample error check on the data that was reported in Step 2. This check can be triggered by the network (step 4) or autonomously at the UE. Autonomous triggering at the UE can be based on, for example, recalibration of the UE's sensor / RF components, changes in data collection methods (e.g., changes in the UE's positioning technology), etc., through which the UE becomes aware of errors in previously reported data. For more examples, see the scenarios described above. The UE can become aware of errors in the various ways described above. Step 6: The UE reports a retrospective error message for the data that has already been reported. Retrospective error information for reported data may include one or more of the following (A–D): A unique identifier for a (potentially) erroneous data sample, or a unique identifier for a group of data samples including a (potentially) erroneous data sample. The timestamp of the collected and / or reported data can be used as the identifier. Each data sample in the dataset can, for example, have a unique record ID. Each sample can also be identified by one of several IDs associated with the protocol session used for data collection, such as session ID, transaction ID, message ID, etc. in LPP or RRC. B) (Multiple) data samples (potentially) contain (multiple) erroneous components. C) Correction of multiple erroneous components in multiple data samples. D) The level of error in the (multiple) error components of (multiple) data samples. Step 7: The network utilizes the retrospective error information indicated by the UE regarding the collected data from the UE. If the collected data has not yet been used for AI / ML operations (e.g., model training), or if the dataset is stored in a repository for future use, the network can correct data samples (e.g., by applying corrections to labels) and / or discard (multiple) erroneous data samples based on the received retrospective error information regarding the collected data. If the dataset with erroneous data samples has already been used, for example, for model training, the network can perform, for example, model retraining or model updates to avoid poor model performance (if the identified errors are believed to affect model performance). In an additional embodiment, with the assistance of the network, the UE that collects data is trained to identify potential errors with respect to the collected data; or equivalently, the UE is trained to assess the probability that the collected data contains errors. In a variant of this embodiment, the UE is provided with conditions or criteria for defining anomalous data. These conditions or criteria are provided via an IE (with) appended at step 4. Figure 2 This is achieved compared to the main implementation. Examples of such conditions (standards) are (1a–1b): 1a. Relevance to Synthetic Data. This involves generating synthetic data from data already collected at the UE, and the acceptable range of the actually collected data. For example, the UE is configured to generate synthetic data for collection time χ based on data collected at times χ-n;…χ-1;χ+1;…χ+n. The UE is also configured with a range in which potentially anomalous data is declared if the data actually collected at time χ is outside this range. In this case, the UE can retrospectively indicate to the network erroneous data collected / reported for past times χ. 1b. In a variant of this method, the UE employs an active approach to replace or supplement the passive approach described above. In the active approach, the UE uses previous data to create synthetic data and then compares it with currently collected data to determine if potential errors exist. However, a drawback of this method is that it requires relatively high computing power at the UE, thus limiting its applicability to a limited number of UE types. Depending on the use case, the network can be a core network entity (such as an LMF for location) or a RAN node, such as a gNB (e.g., for beam management use cases). In another embodiment, Figure 2 The roles of the UE and the network, as shown, can be reversed. That is, the network can collect AI / ML datasets and provide them to the UE, such as... Figure 3 As shown. For convenience (to maintain...) Figure 2 and Figure 3 (The consistency between them), the data provided by the network to the UE is called the data report. Figure 3 The signaling exchange between UE-B 110-B and the network (e.g., RAN node 170 or one or more network elements 190) is shown. In another embodiment, such as Figure 3 The signaling shown, as well as the behavior of the network and the UE, can be Figure 2 The extension, where collected data from one UE (UE-A) is provided by the network to another UE (UE-B), such as... Figure 3 As shown. In this case, the network receives data from UE-B at... Figure 3 The trigger received in step 4 can then lead to a network trigger. Figure 2 UE-A execution Figure 2 The traceability data sample error check in step 4. Further, step 5 (as in step 4) can then be performed at UE-A. Figure 2 (as shown in step 5). The advantages and technical effects of the example described in this paper include: the solution enables the network to recognize errors in the data collected from the user's UE. This allows the network to correct data samples (e.g., by applying corrections to labels) and / or discard (multiple) erroneous data samples to ensure high performance for the operation, for example, before using the dataset for an expected AI / ML operation. Furthermore, if a dataset with erroneous data samples has already been used, the network can perform, for example, retraining the model or updating the model to avoid poor model performance (if the identified errors are considered to affect model performance). Therefore, the examples described in this paper relate to AI / ML data collection and, for example, AI / ML air interface use cases (CSI, beam management, positioning) in Rel-19. Furthermore, the AI / ML data collection enhancements described in this paper can be part of 6G and later versions, including signaling-related aspects. Figure 4 This is an example device 400 that can be implemented in hardware and configured to implement the examples described herein. Device 400 includes at least one processor 402 (e.g., an FPGA and / or a CPU), and one or more memories 404 including computer program code 405 having instructions for performing the methods described herein, wherein at least one memory 404 and computer program code 405 are configured to utilize at least one processor 402 to cause device 400 to implement a circuit system, process, component, module, or function (implemented using control module 406) to implement the examples described herein. The one or more memories 404 may include non-transitory memory, transient memory, volatile memory (e.g., RAM), or non-volatile memory (e.g., ROM). Retrospective error indication or determination 430 implements examples related to retrospective error indication following AI / ML data collection and reporting as described in this article. Device 400 includes a display and / or I / O interface 408, which includes user interface (UI) circuitry and components that can be used to display aspects or states of the methods described herein (e.g., when one of the methods is being performed or at a later time), or to receive input from a user, such as through a keyboard, camera, touchscreen, touch area, microphone, biometrics, one or more sensors, etc. Device 400 includes one or more communication interfaces (e.g., network (N / W) interface (I / F)) 410. The communication interfaces 410 may be wired and / or wireless and may transmit over the Internet / (multiple) other networks via one or more links 424 using any communication technology. The links 424 may be... Figure 1 Links 131 and / or 176 in the (multiple) links. Figure 1The multiple links 131 and / or 176 can also be implemented using multiple transceivers 416 and corresponding multiple wireless links 426. The multiple communication interfaces 410 may include one or more transmitters or one or more receivers. Transceiver 416 includes one or more transmitters 418 and one or more receivers 420. Transceiver 416 and / or (multiple) communication interfaces 410 may include standard, well-known components such as amplifiers, filters, frequency converters, (de)modulators, encoding / decoding circuitry, and one or more antennas, such as antenna 414 for communication via wireless link 426. The control module 406 of device 400 includes one or both of portions 406-1 and / or 406-2, and can be implemented in various ways. Control module 406 can be implemented in hardware as control module 406-1, for example, as part of one or more processors 402. Control module 406-1 can also be implemented as an integrated circuit or by other hardware (e.g., a programmable gate array). In another example, control module 406 can be implemented as control module 406-2, which is implemented as computer program code (with corresponding instructions) 405 and executed by one or more processors 402. For example, one or more memories 404 store instructions that, when executed by one or more processors 402, cause device 400 to perform one or more operations described herein. Furthermore, one or more processors 402, one or more memories 404, and example algorithms (e.g., flowcharts and / or signaling diagrams) encoded as instructions, programs, or code are components that enable the execution of the operations described herein. The device 400 for implementing the control function 406 may be a UE 110, a RAN node 170 (e.g., a gNB), or (multiple) network elements 190 (e.g., an LMF 190). Therefore, processor 402 may correspond to (multiple) processors 120, (multiple) processors 152, and / or (multiple) processors 175; memory 404 may correspond to one or more memories 125, one or more memories 155, and / or one or more memories 171; computer program code 405 may correspond to computer program code 123, computer program code 153, and / or computer program code 173; control module 406 may correspond to module 140-1, module 140-2, module 150-1, and / or module 150-2; (multiple) communication interfaces 410 and / or transceivers 416 may correspond to transceiver 130, (multiple) antennas 128, transceiver 160, (multiple) antennas 158, (multiple) network interfaces 161, and / or (multiple) network interfaces 180. Alternatively, the device 400 and its components may not correspond to the UE 110, RAN node 170, or (multiple) network components 190 and their respective components, because the device 400 may be part of an self-organizing / optimized network (SON) node or other node (e.g., a node in the cloud). Device 400 may also be distributed throughout the network (e.g., 100), including within device 400 and between device 400 and any network element (e.g., network control element (NCE) 190 and / or RAN node 170 and / or UE 110). Interface 412 enables data communication and signaling between the various components of device 400, such as... Figure 4 As shown. For example, interface 412 may be one or more buses, such as an address bus, data bus, or control bus, and may include any interconnection mechanism, such as a series of lines on a motherboard or integrated circuit, optical fiber, or other optical communication device. Computer program code (e.g., instructions) 405 (including control 406) may include object-oriented software configured to pass data or messages between objects within computer program code 405, or computer program code (e.g., instructions) 405 (including control 406) may include functional, scripting, or procedural code. Device 400 need not include every feature mentioned, and may include other features. The various components of device 400 may be at least partially located within a common housing 428, or a subset of the various components of device 400 may be at least partially located within different housings, which may include housing 428. Figure 5Schematic representations of non-volatile storage media 500a (e.g., computer / optical disc (CD) or digital versatile optical disc (DVD)), 500b (e.g., a Universal Serial Bus (USB) memory stick), and 500c (e.g., cloud storage for downloading instructions and / or parameters 502 or receiving email transmission instructions and / or parameters 502) are shown, on which instructions and / or parameters 502 are stored, which, when executed by a processor, cause the processor to perform one or more steps of the methods described herein. Instructions and / or parameters 502 may represent computer-readable media. Figure 6 This is an example method 600 based on the examples described herein. At 610, the method includes collecting data for operation, wherein the operation includes artificial intelligence or machine learning operation. At 620, the method includes reporting the data collected for operation to a network entity. At 630, the method includes determining traceability error information associated with the reported data. At 640, the method includes sending the traceability error information associated with the reported data to a network entity. Method 600 can be performed using UE 110, UE-A 110-A, UE-B 110-B, or device 400. Figure 7 This is an example method 700 based on the examples described herein. At 710, the method includes configuring a user equipment to collect data for operation, wherein the operation includes artificial intelligence or machine learning operations. At 720, the method includes receiving a report from the user equipment, the report including the data collected for operation. At 730, the method includes receiving traceability error information associated with reported data from the user equipment. At 740, the method includes performing an action based on the traceability error information associated with the reported data received from the user equipment. Method 700 can be performed using RAN node 170, one or more network elements 190, or device 400. Figure 8 This is an example method 800 based on the examples described herein. At 810, the method includes receiving a report from a first user equipment, the report including data collected for operation, wherein the operation includes artificial intelligence or machine learning operations. At 820, the method includes sending the reported, collected data for operation to a second user equipment. At 830, the method includes receiving a trigger message from the second user equipment for performing a traceability data error check on the reported data to determine traceability error information. At 840, the method includes sending a trigger message to the first user equipment for performing a traceability data error check on the reported data to determine traceability error information. Method 800 can be performed using RAN node 170, one or more network elements 190, or apparatus 400. Figure 9This is an example method 900 based on the examples described herein. At 910, the method includes receiving data provided by a network entity, including data collected for operation, wherein the operation includes artificial intelligence or machine learning operations. At 920, the method includes receiving traceability error information associated with the provided data from the network entity. At 930, the method includes performing an action based on the traceability error information associated with the provided data received from the network entity. Method 900 can be performed using UE 110, UE-A 110-A, UE-B 110-B, or device 400. Figure 10 This is an example method 1000 based on the examples described herein. At 1010, the method includes collecting data for operation, wherein the operation includes artificial intelligence or machine learning operations. At 1020, the method includes providing the data collected for operation to a user equipment. At 1030, the method includes determining traceability error information associated with the provided data. At 1040, the method includes sending the traceability error information associated with the provided data to the user equipment. Method 1000 can be performed using RAN node 170, one or more network elements 190, or device 400. The following embodiments are provided and described herein. Example 1. An apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to at least: collect data for operation, wherein the operation includes artificial intelligence or machine learning operation; report the data collected for operation to a network entity; determine traceability error information associated with the reported data; and send the traceability error information associated with the reported data to the network entity. Example 2. According to the apparatus of Example 1, the data sample of the reported data includes at least one of the following: measurement, truth label, quality indicator, timestamp. Example 3. An apparatus according to any one of Examples 1 to 2, wherein the traceability error information includes at least one or more of the following: a unique identifier of an erroneous data sample of the reported data, a unique identifier of a set of data samples including the erroneous data sample of the reported data, an erroneous component of a data sample of the reported data, a correction of the erroneous component of a data sample of the reported data, the level of error in the erroneous component of the data sample of the reported data, the actual error in the erroneous component of the data sample of the reported data, an indication that at least one data sample or other aspect of the reported data is potentially erroneous, and a unique identifier of at least one data sample or other aspect of the reported data that is potentially erroneous. Example 4. According to the apparatus of Example 3, wherein an erroneous data sample of the reported data, or a set of data samples including an erroneous data sample of the reported data, or an erroneous component of a data sample of the reported data, or at least one potentially erroneous data sample of the reported data, or other aspects thereof, are identified by at least one or more of the following: a timestamp, a unique record identifier, an identifier of a protocol session for the collection of the reported data, and an identifier of a protocol message for the collection of the reported data. Example 5. An apparatus according to any one of Examples 1 to 4, wherein the apparatus is further configured to: send a unique data identifier to a network entity for each data sample of the reported data collected for operation. Example 6. An apparatus according to any one of Examples 1 to 5, wherein the apparatus is further configured to: receive from a network entity a trigger message for performing a traceability data error check on the reported data to determine traceability error information; wherein the traceability data error check for determining traceability error information is performed in response to receiving the trigger message from the network entity. Example 7. According to the apparatus of Example 6, the trigger message received from the network entity for performing a retrospective data error check on the reported data includes one of the following: a unique identifier of a potentially erroneous data sample of the reported data, or a unique identifier of a set of data samples including a potentially erroneous data sample of the reported data, or a component of a potentially erroneous data sample of the reported data. Example 8. An apparatus according to any one of Examples 1 to 7, wherein the apparatus is further configured to determine traceability error information based on one of the following: recalibration of at least one sensor of the apparatus, recalibration of at least one radio frequency component of the apparatus, change of method for collecting data for operation, change of positioning technology of the apparatus, receiving correction data after collecting the reported data, determination of systematic errors in collecting the reported data. Example 9. An apparatus according to any one of Examples 1 to 8, wherein the traceability error information is based on an error in the reported data, and wherein the apparatus becomes aware of the error in the reported data based on: recalibration of at least one sensor of the apparatus, or receiving correction data after the reported data has been collected, or determining that the reference time used to timestamp the measurement is incorrect, wherein the collected data includes the measurement, or determining a systematic error used to collect the reported data. Example 10. An apparatus according to any one of Examples 1 to 9, wherein the apparatus is trained to identify errors in the collected data. Example 11. An apparatus according to any one of Examples 1 to 10, wherein the apparatus is trained to determine the probability that the collected data has errors. Example 12. An apparatus according to any one of Examples 1 to 11, wherein the apparatus is further configured to: receive from a network at least one criterion for determining anomalous data; wherein traceability error information associated with reported data is determined based on at least one criterion received from the network for determining anomalous data. Example 13. The apparatus according to Example 12, wherein the apparatus is further configured to: perform a traceability data error check to determine traceability error information in response to receiving a trigger message from a network entity; wherein at least one criterion for determining anomalous data is received from the network together with the trigger message to perform a traceability data error check on the reported data. Example 14. An apparatus according to any one of Examples 12 to 13, wherein the apparatus is further configured to: determine that an anomalous data sample of the reported data is outside the range of values, wherein at least one criterion for determining the anomalous data includes a range of values; wherein the traceability error information associated with the reported data is based on determining that an anomalous data sample of the reported data is outside the range of values. Example 15. An apparatus according to any one of Examples 1 to 14, wherein the apparatus is further configured to: receive a first threshold from a network entity, the first threshold being used to determine that a data sample of the reported data is an erroneous data sample. Example 16. The apparatus according to Example 15, wherein the apparatus is further configured to: generate a synthetic data sample at the moment when a data sample of the reported data is collected; determine the difference between the synthetic data sample and the data sample of the reported data; and determine that the data sample of the reported data is an erroneous data sample in response to the difference between the synthetic data sample and the data sample of the reported data being greater than a first threshold; wherein the traceability error information includes information relating to the data sample that is an erroneous data sample. Example 17. An apparatus according to any one of Examples 1 to 16, wherein the apparatus is further configured to: generate a synthetic data sample at the moment the data sample of the reported data is collected; determine the difference between the synthetic data sample and the data sample of the reported data; and determine that the data sample of the reported data is an erroneous data sample in response to the difference between the synthetic data sample and the data sample of the reported data being greater than a second threshold; wherein the traceability error information includes information relating to the data sample that is an erroneous data sample. The first threshold and the second threshold may be the same or different. Example 18. An apparatus according to any one of Examples 1 to 17, wherein: the apparatus is a user equipment, or the apparatus includes a user equipment, or the user equipment includes an apparatus. Example 19. An apparatus according to any one of Examples 1 to 18, wherein the network entity is or includes: a core network entity, or a radio access network node, or a user equipment. Example 20. An apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to at least: configure a user equipment to collect data for operation, wherein the operation includes artificial intelligence or machine learning operation; receive a report from the user equipment, the report including data collected for operation; receive traceability error information associated with reported data from the user equipment; and perform an action based on the traceability error information associated with the reported data received from the user equipment. Example 21. According to the apparatus of Example 20, the data sample of the reported data includes at least one of the following: measurement, truth label, quality indicator, timestamp. Example 22. An apparatus according to any one of Examples 20 to 21, wherein the traceability error information includes at least one or more of the following: a unique identifier of an erroneous data sample of the reported data, a unique identifier of a set of data samples including the erroneous data sample of the reported data, an erroneous component of a data sample of the reported data, a correction of the erroneous component of a data sample of the reported data, the level of error in the erroneous component of the data sample of the reported data, the actual error in the erroneous component of the data sample of the reported data, an indication that at least one data sample or other aspect of the reported data is potentially erroneous, and a unique identifier of at least one data sample or other aspect of the reported data that is potentially erroneous. Example 23. The apparatus according to Example 22, wherein an erroneous data sample of the reported data, or a set of data samples including an erroneous data sample of the reported data, or an erroneous component of a data sample of the reported data, or at least one potentially erroneous data sample of the reported data, or other aspects thereof, is identified by at least one or more of the following: a timestamp, a unique record identifier, an identifier of the protocol session for the collection of the reported data, and an identifier of the protocol message for the collection of the reported data. Example 24. An apparatus according to any one of Examples 20 to 23, wherein the apparatus is further configured to: receive from a user equipment a unique data identifier for each data sample of the reported, collected data for operation; wherein an action performed based on traceability error information for the reported data is performed based on at least one unique data identifier among the unique data identifiers of the data samples of the collected data that have been reported for operation. Example 25. An apparatus according to any one of Examples 20 to 24, wherein the apparatus is further configured to: send a trigger message to a user equipment for performing a traceability data error check on the reported data; wherein traceability error information associated with the reported data is received from the user equipment based on the sending of the trigger message. Example 26. The apparatus according to Example 25, wherein the apparatus is further configured to: determine that there is a potential error in the reported data used for operation, or that the reported data used for operation is potentially corrupted; wherein a trigger message for performing a retrospective data error check on the reported data is sent to the user equipment in response to determining that there is a potential error in the reported data used for operation or that the reported data used for operation is potentially corrupted. Example 27. The apparatus of Example 26, wherein determining that there is a potential error in the reported data used for operation or that the reported data used for operation is potentially corrupted is based on one of the following: using the reported data to monitor the performance of the operation, wherein the operation includes executing a pre-trained artificial intelligence or machine learning model, comparing the reported data with a dataset, and observing the consistency between the reported data and data previously provided by a user device. Example 28. An apparatus according to any one of Examples 25 to 27, wherein a trigger message sent to a user equipment for performing a retrospective data error check on reported data includes one of the following: a unique identifier of a data sample of reported data identified as potentially erroneous, a unique identifier of a set of data samples, a set of data samples including the data sample of reported data identified as potentially erroneous, and a component of the data sample of reported data identified as potentially erroneous. Example 29. An apparatus according to any one of Examples 20 to 28, wherein the action performed based on traceability error information associated with reported data includes one of the following: updating the reported data and performing an operation using the updated reported data; updating an error data sample having an identifier corresponding to an identifier of an error data sample received together with the traceability error information; updating a trained model and performing an operation using the updated trained model, the trained model being trained using the reported data; discarding the error data of the reported data and performing an operation using the reported data without the discarded error data; and sending the traceability error information associated with the reported data to another user equipment. Example 30. An apparatus according to any one of Examples 20 to 29, wherein the apparatus is further configured to: send to a user equipment at least one criterion for determining anomalous data; wherein the traceability error information received from the user equipment associated with the reported data is based on at least one criterion sent to the user equipment for determining anomalous data. Example 31. The apparatus according to Example 30, wherein at least one criterion for determining anomalous data is sent to the user equipment along with a trigger message, the trigger message being used to perform a traceability data error check on the reported data to determine traceability error information. Example 32. An apparatus according to any one of Examples 30 to 31, wherein: at least one criterion for determining anomalous data includes a range of values; and traceability error information associated with the reported data is based on a sample of anomalous data from the reported data that is outside the range of values. Example 33. An apparatus according to any one of Examples 20 to 32, wherein the apparatus is further configured to: send a first threshold to a user equipment, the first threshold being used to determine that a data sample of the reported data is an erroneous data sample. Example 34. The apparatus according to Example 33, wherein: the difference between the synthetic data sample generated at the moment when the data sample of the reported data is collected and the data sample of the reported data is greater than a first threshold, the data sample of the reported data is an erroneous data sample, and the traceability error information includes information related to the data sample being an erroneous data sample. Example 35. An apparatus according to any one of Examples 20 to 34, wherein: the difference between a synthetic data sample generated at the moment when the data sample of the reported data is collected and the data sample of the reported data is greater than a second threshold, the data sample of the reported data is an erroneous data sample, and the traceability error information includes information related to the data sample being an erroneous data sample. Example 36. An apparatus according to any one of Examples 20 to 35, wherein: the apparatus is a core network entity, or the apparatus includes a core network entity, or the core network entity includes the apparatus, or the apparatus is a radio access network node, or the apparatus includes a radio access network node, or the radio access network node includes the apparatus, or the apparatus is a network node, or the apparatus includes a network node, or the network node includes the apparatus. Example 37. An apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to at least: receive a report from a first user equipment, the report including data collected for operation, wherein the operation includes artificial intelligence or machine learning operation; transmit the reported, collected data for operation to a second user equipment; receive from the second user equipment a trigger message for performing a traceability data error check on the reported data to determine traceability error information; and transmit to the first user equipment a trigger message for performing a traceability data error check on the reported data to determine traceability error information. Example 38. The apparatus according to Example 37, wherein the traceability error information includes at least one or more of the following: a unique identifier of an erroneous data sample of the reported data, a unique identifier of a set of data samples including an erroneous data sample of the reported data, an erroneous component of a data sample of the reported data, a correction of an erroneous component of a data sample of the reported data, the level of error in an erroneous component of a data sample of the reported data, the actual error in an erroneous component of a data sample of the reported data, an indication that at least one data sample or other aspect of the reported data may be erroneous, and a unique identifier of at least one data sample or other aspect of the reported data that may be erroneous. Example 39. An apparatus according to any one of Examples 37 to 38, wherein the apparatus is further configured to: receive traceability error information from a first user equipment; and send traceability error information to a second user equipment. Example 40. The apparatus according to Example 39, wherein: a trigger message received from a second user equipment and sent to a first user equipment includes an identifier of a potentially erroneous data sample of the reported data; and traceability error information received from the first user equipment and sent to the second user equipment includes an update of the potentially erroneous data sample of the reported data. Example 41. An apparatus according to any one of Examples 37 to 40, wherein: the apparatus is a core network entity, or the apparatus includes a core network entity, or the core network entity includes the apparatus, or the apparatus is a radio access network node, or the apparatus includes a radio access network node, or the radio access network node includes the apparatus, or the apparatus is a network node, or the apparatus includes a network node, or the network node includes the apparatus. Example 42. A method comprising: collecting data for an operation, wherein the operation includes artificial intelligence or machine learning operations; reporting the data collected for the operation to a network entity; identifying traceability error information associated with the reported data; and sending the traceability error information associated with the reported data to the network entity. Example 43. A method comprising: configuring a user device to collect data for operation, wherein the operation includes artificial intelligence or machine learning operation; receiving a report from the user device, the report including the data collected for the operation; receiving traceability error information associated with the reported data from the user device; and performing an action based on the traceability error information associated with the reported data received from the user device. Example 44. A method comprising: receiving a report from a first user equipment, the report including data collected for operation, wherein the operation includes artificial intelligence or machine learning operation; sending the reported, collected data for operation to a second user equipment; receiving from the second user equipment a trigger message for performing a traceability data error check on the reported data to determine traceability error information; and sending the trigger message to the first user equipment for performing a traceability data error check on the reported data to determine traceability error information. Example 45. An apparatus comprising: components for collecting data for operation, wherein the operation includes artificial intelligence or machine learning operation; components for reporting the collected data for operation to a network entity; components for determining traceability error information associated with the reported data; and components for sending the traceability error information associated with the reported data to the network entity. Example 46. An apparatus comprising: components for configuring a user equipment to collect data for operation, wherein the operation includes artificial intelligence or machine learning operation; components for receiving a report from the user equipment, the report including the data collected for operation; components for receiving traceability error information associated with the reported data from the user equipment; and components for performing an action based on the traceability error information associated with the reported data received from the user equipment. Example 47. An apparatus comprising: components for receiving a report from a first user equipment, the report including data collected for operation, wherein the operation includes artificial intelligence or machine learning operation; components for sending the reported, collected data for operation to a second user equipment; components for receiving from the second user equipment a trigger message for performing a traceability data error check on the reported data to determine traceability error information; and components for sending the trigger message to the first user equipment for performing a traceability data error check on the reported data to determine traceability error information. Example 48. A computer-readable medium including instructions stored thereon for performing at least the following operations: collecting data for operations, wherein the operations include artificial intelligence or machine learning operations; reporting the data collected for operations to a network entity; determining traceability error information associated with the reported data; and sending the traceability error information associated with the reported data to the network entity. Example 49. A computer-readable medium including instructions stored thereon for performing at least the following: configuring a user equipment to collect data for operation, wherein the operation includes artificial intelligence or machine learning operation; receiving a report from the user equipment, the report including data collected for operation; receiving traceability error information associated with reported data from the user equipment; and performing an action based on the traceability error information associated with reported data received from the user equipment. Example 50. A computer-readable medium including instructions stored thereon for performing at least the following: receiving a report from a first user equipment, the report including data collected for operation, wherein the operation includes artificial intelligence or machine learning operation; sending the reported, collected data for operation to a second user equipment; receiving from the second user equipment a trigger message for performing a traceability data error check on the reported data to determine traceability error information; and sending to the first user equipment a trigger message for performing a traceability data error check on the reported data to determine traceability error information. Example 51. An apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to at least: receive data provided by a network entity, the data including data collected for operation, wherein the operation includes artificial intelligence or machine learning operations; receive traceability error information associated with the provided data from the network entity; and perform an action based on the traceability error information associated with the provided data received from the network entity. Example 52. According to the apparatus of Example 51, the data sample of the provided data includes at least one of the following: measurement, truth label, quality indicator, timestamp. Example 53. An apparatus according to any one of Examples 51 to 52, wherein the traceability error information includes at least one or more of the following: a unique identifier of an erroneous data sample of the provided data, a unique identifier of a set of data samples including an erroneous data sample of the provided data, an erroneous component of a data sample of the provided data, a correction of an erroneous component of a data sample of the provided data, the level of error in an erroneous component of a data sample of the provided data, the actual error in an erroneous component of a data sample of the provided data, an indication that at least one data sample or other aspect of the provided data may be erroneous, and a unique identifier of at least one data sample or other aspect of the provided data that may be erroneous. Example 54. The apparatus according to Example 53, wherein an erroneous data sample of already provided data, or a set of data samples including an erroneous data sample of already provided data, or an erroneous component of a data sample of already provided data, or at least one potentially erroneous data sample of already provided data, or other aspects thereof, is identified by at least one or more of the following: a timestamp, a unique record identifier, an identifier of a protocol session for the collection of already provided data, and an identifier of a protocol message for the collection of already provided data. Example 55. An apparatus according to any one of Examples 51 to 54, wherein the apparatus is further configured to: receive from a network entity a unique data identifier for each data sample of collected data provided for operation; wherein an action performed based on traceability error information for the provided data is performed based on at least one unique data identifier among the unique data identifiers for the data samples of collected data provided for operation. Example 56. An apparatus according to any one of Examples 51 to 55, wherein the apparatus is further configured to: send a trigger message to a network entity for performing a traceability data error check on the data already provided; wherein traceability error information associated with the data already provided is received from the network entity based on the sending of the trigger message. Example 57. The apparatus according to Example 56, wherein the apparatus is further configured to: determine that there is a potential error in the provided data for operation or that the provided data for operation is potentially corrupted; wherein a trigger message for performing a traceable data error check on the provided data is sent to the network entity in response to determining that there is a potential error in the provided data for operation or that the provided data for operation is potentially corrupted. Example 58. The apparatus according to Example 57, wherein determining that there is a potential error in the already provided data for operation or that the already provided data for operation is potentially corrupted is based on one of the following: using the already provided data to monitor the performance of the operation, wherein the operation includes executing a pre-trained artificial intelligence or machine learning model, comparing the already provided data with a dataset, and observing the consistency between the already provided data and data previously provided by the network entity. Example 59. An apparatus according to any one of Examples 56 to 58, wherein a trigger message sent to a network entity for performing a traceable data error check on provided data includes one of the following: a unique identifier of a data sample of provided data identified as potentially erroneous, and a unique identifier of a set of data samples, the set of data samples including the data sample of provided data identified as potentially erroneous, and the components of the data sample of provided data identified as potentially erroneous. Example 60. An apparatus according to any one of Examples 51 to 59, wherein the action performed based on traceability error information associated with already provided data includes one of the following: updating the already provided data and performing an operation using the updated already provided data; updating an error data sample having an identifier corresponding to an identifier of an error data sample received together with the traceability error information; updating a trained model and performing an operation using the updated trained model, the trained model being trained using the already provided data; discarding error data from the already provided data and performing an operation using the already provided data without the discarded error data; and sending traceability error information associated with the already provided data to a user equipment. Example 61. An apparatus according to any one of Examples 51 to 60, wherein the apparatus is further configured to: send to a network entity at least one criterion for determining anomalous data; wherein traceability error information received from the network entity in connection with data already provided is based on at least one criterion sent to the network entity for determining anomalous data. Example 62. The apparatus according to Example 61, wherein at least one criterion for determining anomalous data is sent to a network entity along with a trigger message, the trigger message being used to perform a traceability data error check on the data already provided to determine traceability error information. Example 63. An apparatus according to any one of Examples 61 to 62, wherein: at least one criterion for determining anomalous data includes a range of values; and traceability error information associated with the provided data is based on a sample of anomalous data from the provided data that is outside the range of values. Example 64. An apparatus according to any one of Examples 51 to 63, wherein the apparatus is further configured to: send a first threshold to a network entity, the first threshold being used to determine that a data sample of the provided data is an erroneous data sample. Example 65. The apparatus according to Example 64, wherein: the difference between the synthetic data sample generated at the moment when the data sample of the already provided data is collected and the data sample of the already provided data is greater than a first threshold, the data sample of the already provided data is an erroneous data sample, and the traceability error information includes information related to the data sample being an erroneous data sample. Example 66. An apparatus according to any one of Examples 51 to 65, wherein: the difference between a synthetic data sample generated at the moment when a data sample of the already provided data is collected and a data sample of the already provided data is greater than a second threshold, the data sample of the already provided data is an erroneous data sample, and the traceability error information includes information related to the data sample being an erroneous data sample. The first threshold and the second threshold may be the same or different. Example 67. An apparatus according to any one of Examples 51 to 66, wherein: the apparatus is a user equipment, or the apparatus includes a user equipment, or the user equipment includes an apparatus. Example 68. An apparatus according to any one of Examples 51 to 67, wherein the network entity is or includes: a core network entity, or a radio access network node, or a user equipment. Example 69. An apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus to at least: collect data for operation, wherein the operation includes artificial intelligence or machine learning operations; provide the collected data for operation to a user equipment; determine traceability error information associated with the provided data; and send the traceability error information associated with the provided data to the user equipment. Example 70. The apparatus according to Example 69, wherein the data sample of the provided data includes at least one of the following: measurement, truth label, quality indicator, timestamp. Example 71. An apparatus according to any one of Examples 69 to 70, wherein the traceability error information includes at least one or more of the following: a unique identifier of an erroneous data sample of the provided data, and a unique identifier of a set of data samples including the erroneous data sample of the provided data, and an erroneous component of a data sample of the provided data, and a correction of the erroneous component of the data sample of the provided data, and the level of error in the erroneous component of the data sample of the provided data, the actual error in the erroneous component of the data sample of the provided data, an indication that at least one data sample or other aspect of the provided data is potentially erroneous, and a unique identifier of at least one data sample or other aspect of the provided data that is potentially erroneous. Example 72. The apparatus according to Example 71, wherein an erroneous data sample of already provided data, or a set of data samples including an erroneous data sample of already provided data, or an erroneous component of a data sample of already provided data, or at least one data sample or other aspect of a potentially erroneous data of already provided data is identified by at least one or more of the following: a timestamp, a unique record identifier, an identifier of a protocol session for the collection of already provided data, and an identifier of a protocol message for the collection of already provided data. Example 73. An apparatus according to any one of Examples 69 to 72, wherein the apparatus is further configured to: send to a user equipment a unique data identifier for each data sample of the collected data provided for operation. Example 74. An apparatus according to any one of Examples 69 to 73, wherein the apparatus is further configured to: receive from a user equipment a trigger message for performing a traceability data error check on the provided data to determine traceability error information; wherein, in response to receiving the trigger message from the user equipment, the traceability data error check for determining traceability error information is performed. Example 75. The apparatus of Example 74, wherein a trigger message received from a user equipment for performing a traceable data error check on provided data includes one of the following: a unique identifier of the provided data that is identified as a potentially erroneous data sample, and a unique identifier of a set of data samples, the set of data samples including the provided data containing potentially erroneous data samples, and the components of the provided data containing potentially erroneous data samples. Example 76. An apparatus according to any one of Examples 69 to 75, wherein the apparatus is further configured to determine traceability error information based on one of the following: recalibration of at least one sensor of the apparatus, recalibration of at least one radio frequency component of the apparatus, change of method for collecting data for operation, change of positioning technology for generating the provided data, receiving verification data after collecting the provided data, and determining a systematic error in collecting the provided data. Example 77. An apparatus according to any one of Examples 69 to 76, wherein the traceability error information is based on an error in already provided data, and wherein the apparatus learns of the error in the already provided data based on: recalibration of at least one sensor of the apparatus, or receiving verification data after the already provided data has been collected, or determining that the reference time used to timestamp the measurement results is incorrect, wherein the already collected data includes the measurement results, or determining a systematic error in the collection of the already provided data. Example 78. An apparatus according to any of Examples 69 to 77, wherein the apparatus is trained to identify errors in the collected data. Example 79. An apparatus according to any of Examples 69 to 78, wherein the apparatus is trained to determine the probability that the collected data has errors. Example 80. An apparatus according to any one of Examples 69 to 79, wherein the apparatus is further configured to: receive from a user equipment at least one criterion for determining anomalous data; wherein traceability error information associated with the provided data is determined based on at least one criterion received from the user equipment for determining anomalous data. Example 81. The apparatus according to Example 80, wherein the apparatus is further configured to: perform a traceability data error check to determine traceability error information in response to receiving a trigger message from a user equipment; wherein at least one criterion for determining abnormal data is received from the user equipment together with the trigger message to perform a traceability data error check on the data already provided. Example 82. An apparatus according to any one of Examples 80 to 81, wherein the apparatus is further configured to: determine that an anomalous data sample of the provided data is outside the range of values, wherein at least one criterion for determining the anomalous data includes a range of values; wherein the traceability error information associated with the provided data is based on determining that an anomalous data sample of the provided data is outside the range of values. Example 83. An apparatus according to any one of Examples 69 to 82, wherein the apparatus is further configured to: receive a first threshold from a user equipment, the first threshold being used to determine that a data sample of the provided data is an erroneous data sample. Example 84. The apparatus according to Example 83, wherein the apparatus is further configured to: generate a synthetic data sample at the moment when a data sample of the already provided data is collected; determine the difference between the synthetic data sample and the data sample of the already provided data; and determine that the data sample of the already provided data is an erroneous data sample in response to the difference between the synthetic data sample and the data sample of the already provided data being greater than a first threshold; wherein the traceability error information includes information related to the data sample being an erroneous data sample. Example 85. An apparatus according to any one of Examples 69 to 84, wherein the apparatus is further configured to: generate a synthetic data sample at the moment when a data sample of the already provided data is collected; determine the difference between the synthetic data sample and the data sample of the already provided data; and determine that the data sample of the already provided data is an erroneous data sample in response to the difference between the synthetic data sample and the data sample of the already provided data being greater than a second threshold; wherein the traceability error information includes information related to the data sample being an erroneous data sample. Example 86. An apparatus according to any one of Examples 69 to 85, wherein: the apparatus is a core network entity, or the apparatus includes a core network entity, or the core network entity includes the apparatus, or the apparatus is a radio access network node, or the apparatus includes a radio access network node, or the radio access network node includes the apparatus, or the apparatus is a network node, or the apparatus includes a network node, or the network node includes the apparatus. Example 87. A method comprising: receiving data provided by a network entity, the data including data collected for operation, wherein the operation includes artificial intelligence or machine learning operation; receiving traceability error information associated with the provided data from the network entity; and performing an action based on the traceability error information associated with the provided data received from the network entity. Example 88. A method comprising: collecting data for an operation, wherein the operation includes artificial intelligence or machine learning operations; providing the data collected for the operation to a user device; determining traceability error information associated with the provided data; and sending the traceability error information associated with the provided data to the user device. Example 89. An apparatus comprising: components for receiving data provided by a network entity from the network entity, the data including data collected for operation, wherein the operation includes artificial intelligence or machine learning operation; components for receiving traceability error information associated with the provided data from the network entity; and components for performing an action based on the traceability error information associated with the provided data received from the network entity. Example 90. An apparatus comprising: components for collecting data for operation, wherein the operation includes artificial intelligence or machine learning operation; components for providing the collected data for operation to a user device; components for determining traceability error information associated with the provided data; and components for sending the traceability error information associated with the provided data to the user device. Example 91. A computer-readable medium including instructions stored thereon for performing at least the following operations: receiving data provided by a network entity, the data including data collected for operation, wherein the operation includes artificial intelligence or machine learning operation; receiving traceability error information associated with the provided data from the network entity; and performing an action based on the traceability error information associated with the provided data received from the network entity. Example 92. A computer-readable medium including instructions stored thereon for performing at least the following operations: collecting data for operations, wherein the operations include artificial intelligence or machine learning operations; providing the collected data for operations to a user device; determining traceability error information associated with the provided data; and sending the traceability error information associated with the provided data to the user device. References to "computer," "processor," etc., should be understood to include not only computers with different architectures, such as single-processor / multi-processor architectures and sequential or parallel architectures, but also special-purpose circuits, such as field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), signal processing devices, and other processing circuits. References to computer programs, instructions, code, etc., should be understood to include software or firmware used with programmable processors, such as the programmable content of hardware devices, whether it is the processor's instructions or the configuration settings of fixed-function devices, gate arrays, or programmable logic devices. The memory described herein can be implemented using any suitable data storage technology, such as semiconductor-based memory devices, flash memory, magnetic storage devices and systems, optical storage devices and systems, non-transitory memory, transient memory, fixed memory, and removable memory. The memory may include a database for storing data. The term “non-transient” as used in this article refers to the limitations of the medium itself (i.e., tangible, not signal-based), rather than limitations on the persistence of data storage (e.g., RAM compared to ROM). As used herein, the term "circuit system" may refer to: (a) a hardware circuit implementation, such as an implementation in an analog circuit system and / or a digital circuit system; and (b) a combination of circuitry and software (and / or firmware), such as (if applicable): (i) a combination of processors, or (ii) a portion of a processor / software comprising a digital signal processor, software, and memory, which work together to enable a device to perform various functions; and (c) circuitry that requires software or firmware to function, such as a microprocessor or a portion thereof, even if the software or firmware is not physically present. As a further example, the term "circuit system" as used herein may also cover an implementation consisting solely of a processor (or a plurality of processors) or a portion thereof and its accompanying software and / or firmware. The term "circuit system" may also cover, for example (if applicable), baseband integrated circuits or application processor integrated circuits for mobile phones, or similar integrated circuits in servers, cellular network equipment, or other network equipment. It should be understood that the above description is illustrative only. Those skilled in the art can devise various alternatives and modifications. For example, the features recited in the dependent claims can be combined with each other in any suitable combination. Furthermore, features of the different embodiments described above can be selectively combined to form a new embodiment. Therefore, this description is intended to cover all such alternatives, modifications, and variations falling within the scope of the appended claims. The following abbreviations and abbreviations that may appear in this specification and / or accompanying drawings are defined as follows (these abbreviations and abbreviations may be connected to each other or combined with other characters, such as using hyphens, forward slashes, letters or numbers, and may be case-insensitive): 3GPP - Third Generation Partnership Project 4G – Fourth Generation 5G – the fifth generation 5GC – 5G Core Network 6G – Sixth Generation AI - Artificial Intelligence AI / ML — Artificial Intelligence / Machine Learning AMF – Access and Mobility Management Function ASIC — Application-Specific Integrated Circuit CD - Optical Disc / Computer Disk CIR—Channel Impulse Response CPU — Central Processing Unit CSI – Channel State Information CU — Central Unit or Centralized Unit DC - Dual Connection DL - Downlink DSP—Digital Signal Processor DU—Distributed Unit DVD – Digital Multifunction Optical Disc; eNB – Evolved Node B (e.g., LTE base station) EN-DC—E-UTRAN New Radio—Dual-Connectivity en-gNB—provides NR user plane and control plane protocol termination to the UE and acts as a secondary node in EN-DC. EPC – Evolution Group Core E-UTRA – Evolved UMTS Terrestrial Radio Access, also known as LTE Radio Access Technology E-UTRAN——E-UTRA network F1 – Interface between CU and DU FG – Feature Group FPGA—Field-Programmable Gate Array (gNB)—is a general-purpose node B used in 5G / NR base stations. It provides NR user plane and control plane protocol termination to the UE and connects to the 5GC node via the NG interface. GNSS—Global Navigation Satellite System IAB – Integrated Access and Backhaul ID — Identifier or identifier I / F — Interface I / O — Input / Output LCM – Lifecycle Management LMF – Location Management Function LOS - Line of Sight LPP – LTE Location Protocol LTE – Long Term Evolution (4G) MAC – Media Access Control ML - Machine Learning MME – Mobility Management Entity MRO – Mobility Robustness Optimization NCE – Network Control Element; ng or NG – Next Generation ng-eNB; NG-RAN – Next Generation Radio Access Network NLOS - Non-line-of-sight NR - New Radio NW - Network N / W — Network OAM – Operation, Management and Maintenance / Operation and Management OTT - Over-the-top PDA - Personal Digital Assistant PDCP – Packet Data Convergence Protocol PHY — Physical Layer PRS—Positioning Reference Signal PRU – Location Reference Unit RAM—Random Access Memory RAN—Radio Access Network Rel — Version (Release) RF - Radio Frequency RLC – Radio Link Control ROM - Read-Only Memory RRC – Radio Resource Control RS—Reference Signal RSRPP—Receive Reference Signal Path Power RU—Radio Unit Rx — Receiver, or receiver S1 – The interface between the Mobility Management Entity (MME) in EPC and the Evolved Node B in E-UTRAN. SDAP - Service Data Adaptation Protocol SGW - Service Gateway SMF – Session Management Function SON – Self-Organizing / Optimizing Network SRS—Detection Reference Signal TBS – Ground Beacon System TRP - Transmitter / Receiver Point Tx — Transmission, or transmitter UAV - Unmanned Aerial Vehicle UE – User Equipment (e.g., wireless, typically mobile devices) UI – User Interface UMTS – Universal Mobile Telecommunications System UPF - User Face Function USB - Universal Serial Bus X2 – Network interface between RAN nodes and between RAN and core network

[0019] Xn — Network interface between NG-RAN nodes

Claims

1. An apparatus for communication, comprising: at least one processor; and at least one memory that stores instructions, which when executed by the at least one processor, cause the apparatus at least to: receive, from a network entity, data provided by the network entity, the data comprising data collected for an operation, wherein the operation comprises an artificial intelligence or machine learning operation; receive, from the network entity, retroactive error information associated with the data that has been provided; and perform an action based on the retroactive error information associated with the data that has been provided received from the network entity.

2. The apparatus of claim 1, wherein a data sample of the data that has been provided comprises at least one of: a measurement, a true value label, a quality indicator, a timestamp.

3. The apparatus of claim 1, wherein the retroactive error information comprises at least one or more of: a unique identification of an erroneous data sample of the data that has been provided, a unique identification of a set of data samples comprising an erroneous data sample of the data that has been provided, an erroneous component of a data sample of the data that has been provided, a correction to an erroneous component of a data sample of the data that has been provided, a level of error in an erroneous component of a data sample of the data that has been provided, an actual error in an erroneous component of a data sample of the data that has been provided, an indication that at least one data sample or other aspect of the data that has been provided is potentially erroneous, a unique identification of at least one data sample or other aspect of the data that has been provided that is potentially erroneous.

4. The apparatus of claim 3, wherein the erroneous data sample of the data that has been provided, or the set of data samples comprising the erroneous data sample of the data that has been provided, or the erroneous component of the data sample of the data that has been provided, or the at least one data sample or other aspect of the data that has been provided that is potentially erroneous is identified with at least one or more of: a timestamp, a unique record identifier, an identifier of a protocol session for the collection of the data that has been provided, an identifier of a protocol message for the collection of the data that has been provided.

5. The apparatus of claim 1, wherein the apparatus is further caused to: receive, from the network entity, a unique data identifier for each data sample of the collected data provided for the operation; wherein the action performed based on the retroactive error information for the data that has been provided is performed based on at least one of the unique data identifiers for the data sample of the collected data provided for the operation.

6. The apparatus of claim 1, wherein the apparatus is further caused to: send, to the network entity, a trigger message for performing a retroactive data error check of the data that has been provided; wherein the traceability error information associated with the provided data is received from the network entity based on the sending of the trigger message.

7. The apparatus of claim 6, wherein the apparatus is further caused to: determine that there is a potential error within the provided data for the operation or that the provided data for the operation is potentially corrupted; wherein the trigger message for performing the traceability data error check of the provided data is sent to the network entity in response to determining that there is the potential error within the provided data for the operation or that the provided data for the operation is potentially corrupted.

8. The apparatus of claim 7, wherein determining that there is the potential error within the provided data for the operation or that the provided data for the operation is potentially corrupted is based on one of: monitoring performance of the operation using the provided data, wherein the operation comprises executing a pre-trained artificial intelligence or machine learning model, comparing the provided data to a dataset, observing consistency between the provided data and data previously provided by the network entity.

9. The apparatus of any one of claims 6 to 8, wherein the trigger message sent to the network entity for performing the traceability data error check of the provided data comprises one of: a unique identification of a data sample of the provided data that is determined to be potentially erroneous, and a unique identification of a group of data samples, the group of data samples comprising a data sample of the provided data that is determined to be potentially erroneous, a component of a data sample of the provided data that is determined to be potentially erroneous.

10. The apparatus of claim 1, wherein the action performed based on the traceability error information associated with the provided data comprises one of: updating the provided data and performing the operation using the updated provided data, updating an erroneous data sample having an identifier corresponding to an identifier of the erroneous data sample received with the traceability error information, updating a trained model, the trained model trained using the provided data, and performing the operation using the updated trained model, discarding an erroneous data of the provided data and performing the operation using the provided data without the discarded erroneous data, sending the traceability error information associated with the provided data to a user device.

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