Method for ai decision transparency and accountability tracing in intelligent unmanned laboratory

Abstract

The application provides an AI decision transparency and responsibility tracing method in an intelligent unmanned laboratory, and relates to the field of intelligent unmanned laboratories. Laboratory equipment, data, algorithm nodes, etc. are abstracted into a standard ontology structure, and process logs and decision links are analyzed in real time to form a traceable knowledge base and risk flow atlas. Natural language generation is used to generate explanation reports, which are convenient for supervision and review; key data and operations are implemented through hash fingerprints and blockchain mechanisms to achieve full-link tamper-proof tracing. Combined with multi-expert review and closed-loop optimization algorithm, responsibility weight allocation and emergency plan suggestions are automatically completed to ensure the transparency of unmanned laboratory decision-making and the accurate attribution of accident responsibility.

Description

Technical Field

[0001] This invention relates to the field of intelligent unmanned laboratories, and more specifically to a method for AI decision-making transparency and accountability in intelligent unmanned laboratories. Background Technology

[0002] With the rapid development of new-generation information technology, the testing and inspection industry is accelerating its transformation towards intelligent manufacturing, and new models such as "lights-out" and unmanned laboratories are constantly emerging. These models, characterized by "unattended operation, high integration, and AI decision-making," automate experimental processes, enable intelligent interconnection of equipment, and facilitate data-driven decision-making, thereby improving industry efficiency, accuracy, and innovation. They represent an important future development direction for the industry.

[0003] Especially in complex scenarios involving equipment malfunctions, data breaches, and AI inference biases, existing systems lack sufficient traceability for critical operations, posing significant risks of data tampering and blame-shifting, and failing to provide sufficient and immutable digital evidence for incident response and liability determination. Furthermore, laboratory operations involve multidisciplinary team collaboration and multiple stakeholders; existing technologies lack effective mechanisms for integrating expert knowledge and allocating weights, making it difficult to meet the objectivity and rationality requirements of liability delineation in complex social and business scenarios. Therefore, there is an urgent need for an integrated intelligent unmanned laboratory risk prevention and traceability system that combines the immutability of blockchain, hash fingerprint tracking, intelligent decision support, and multi-expert collaborative optimization to improve the transparency, traceability, and scientific rigor of laboratory operations and risk response.

[0004] However, traditional quality management systems, based on manual operation, paper records, and experience-based judgment, are ill-suited to the new characteristics of intelligent unmanned laboratories. Under automation and data-driven models, traditional systems reveal problems such as a lack of adaptability, outdated management mechanisms, insufficient regulatory effectiveness, and inadequate safety guarantees, thus hindering the application and development of intelligent unmanned laboratories. Summary of the Invention

[0005] This invention aims to address the technical problems existing in current intelligent unmanned laboratories during risk prevention and accountability, such as unclear attribution of responsibility, broken data traceability chains, difficulty in efficient collaboration among multiple parties in handling disputes, low integration of expert knowledge, and insufficient objectivity in multi-stage decision-making. It aims to achieve fully automated management of laboratory anomalies and system defects, transparent allocation of responsibility chains, and optimization of scientific and operable risk emergency and prevention mechanisms.

[0006] To achieve the above objectives, the present invention employs the following technical solution: the method comprising: Laboratory element mapping and risk control modeling: Construct an ontology system of key laboratory elements adapted to intelligent unmanned scenarios. Employ a dynamic risk relationship graph construction algorithm to organically link physical entities, algorithm decision nodes, and data flow links in the laboratory, forming a dynamic mapping model of multi-dimensional elements. Based on the accountability of AI decision-making transparency, the input features, decision-making logic process and output results of the AI ​​model are automatically recorded in layers, and supplemented by a natural language generation module to output understandable reports that are easy to supervise and review. Based on multi-source data tracing and anomaly accountability, blockchain technology is combined with hash tracing mechanism to establish a unique and tamper-proof identity fingerprint for every key data, every device operation, and every AI decision call. The element-process-responsibility closed-loop optimization adopts a responsibility allocation decision-making assistance algorithm with multiple expert perspectives. It integrates historical expert review data, scenario cases, and multiple weights to automatically suggest reasonable responsibility allocation weights and response measures.

[0007] In one solution, the laboratory element mapping and risk control modeling includes: constructing a key laboratory element ontology system for intelligent unmanned scenarios, by abstracting modules such as equipment, data, environmental status, algorithm models, control commands, sensor acquisition, and AI interpretation results into ontology nodes, and assigning role labels and behavior tags to each node; Using a graph theory structure, nodes are sets of laboratory elements, edges are interaction dependencies or process-driven relationships between elements, and relations are entity attributes and categories. It parses multi-source input data such as process logs, equipment operation data, AI decision-making links, and user operation sequences to generate a standardized triple knowledge base, and ensures the standardization and uniformity of the ontology through entity disambiguation and attribute alignment.

[0008] In one approach, the accountability traceability based on AI decision transparency includes: automatically and hierarchically recording the input features, decision logic process, and output results of the AI ​​model, and using a natural language generation module to output an explanatory report that can be monitored and reviewed; based on the hierarchical recording mechanism, synchronously encoding and archiving the original input features, model inference chain, result output, and operating environment metadata; and automatically archiving each feature and its source, collection link, preprocessing history, anomaly identification tags, etc., in the input feature tracking stage to ensure the traceability of the data link. During the AI ​​model inference phase, information such as internal state evolution, decision path branches, and activation weights is captured, and the granularity and depth of the interpretation report are automatically controlled according to the transparency level classification scheme. All link data is transformed into structured natural language reports through the semantic rendering generation module, enabling a fully transparent interpretation of AI decision-making results and supporting automated support for input anomaly tracing, responsibility identification, and risk control.

[0009] In one solution, the multi-source data tracing and anomaly attribution method includes: using a full-link data fingerprinting and anomaly attribution algorithm to integrate blockchain technology with a hash tracing mechanism; establishing a unique and tamper-proof identity fingerprint for each key data item, each device operation, and each AI decision call; and encapsulating all data collection, entry, processing, transmission, and storage nodes into a unified meta-transaction structure, including a unique sequence number, timestamp, operation subject, business type, data source, flow destination, content summary, hash signature, preceding transaction hash, and security signature. Each data action forms a blockchain structure through a multi-strategy hash compression algorithm to achieve sequential on-chain connection. Device operation logs and AI inference records are all uploaded to the blockchain simultaneously. External interface calls are uniformly incorporated into the global traceability map through signatures and numbers, ensuring identity fusion and unique action identification across the entire chain.

[0010] In one solution, the aforementioned element-process-responsibility closed-loop optimization includes: introducing a responsibility allocation decision-making assistance algorithm with multiple expert perspectives, combining historical expert review data, scenario cases and multi-party weights, automatically suggesting reasonable responsibility attribution weights and corresponding measures, covering theoretical, technical and management three-level iteration and closed-loop optimization; Through multiple rounds of anonymous consultation and information feedback processes, and by integrating multidisciplinary review by laboratory management, AI technology experts, legal compliance and regulatory officials, collaborative judgments are made on the allocation of responsibility chains for key process nodes, the closed-loop emergency response for anomalies, and the interpretability of on-chain attribution results.

[0011] In one approach, the laboratory element mapping and risk control modeling includes: dynamically and continuously supplementing and iteratively updating the causal relationship of risks based on a time-series data sliding window mechanism, realizing automatic chain building of causal transmission of abnormal nodes, generating a dynamic risk relationship map covering the entire process, and realizing data marking and transparency of responsibility processes for the entire life cycle of key laboratory elements.

[0012] In one approach, the multi-source data tracing and anomaly accountability system detects data anomalies or AI decision-making biases by capturing deviation points in the anomaly detection module. This automatically triggers the anomaly accountability workflow, tracing back the original data and responsibility chain along the blockchain. Combined with signature tracing and attribution determination on the blockchain, it assists in accurately locating responsibility attribution and provides full-process digital evidence protection for accident emergency response and responsibility determination.

[0013] In one approach, the aforementioned element-process-responsibility closed-loop optimization includes: assembling an expert group using an algorithm, organizing the organization to propose weighted suggestions for the attribution of responsibility and emergency measures for each element chain node, and achieving dynamic optimization of responsibility allocation suggestions through normalization, sample weighting, and historical case parameter tuning mechanisms.

[0014] Beneficial effects of this invention: This invention provides an AI-based method for decision-making transparency and accountability in intelligent unmanned laboratories, which enables efficient mapping of core laboratory elements, transparent risk control modeling, and full-process accountability management.

[0015] On the one hand, by constructing a multi-dimensional ontology system and dynamic graph, and comprehensively linking equipment, data, algorithms, and processes, dynamic visualization of laboratory elements and automatic tracing of risk causality are achieved, effectively improving the timeliness of abnormal event identification and early warning. On the other hand, based on the hierarchical recording of the entire AI decision-making process and the natural language interpretation mechanism, the input, decision, and output results of the AI ​​model can be automatically generated into structured reports, providing intuitive, easy-to-understand, and verifiable transparent evidence for regulatory and compliance inspections.

[0016] Furthermore, by integrating blockchain and hash traceability mechanisms, all key data and operational processes are uniquely fingerprinted throughout the entire process, ensuring the immutability of laboratory data and the traceability of operational activities, providing a solid data foundation for emergency response and liability determination. Simultaneously, a multi-expert perspective liability allocation algorithm, combined with multi-party weighting and case adjustments, automatically optimizes liability attribution and response measures, promoting a closed loop of theory, technology, and management, and significantly enhancing the risk management and liability determination capabilities of the intelligent unmanned laboratory. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart illustrating the accountability backtracking process based on AI decision transparency, as presented in this invention. Detailed Implementation

[0018] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0019] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. To facilitate understanding, the invention will now be described more fully with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the invention more thorough and complete.

[0020] like Figure 1 As shown, a method for AI decision-making transparency and accountability in an intelligent unmanned laboratory includes the following steps: Step 1: Laboratory Element Mapping and Risk Control Modeling First, based on the element transfer logic from "human-machine-material" to "algorithm-data-equipment," a key element ontology system for laboratories adapted to intelligent unmanned scenarios is constructed. A "dynamic risk relationship graph construction algorithm" is introduced. This algorithm, based on knowledge graph technology and causal reasoning, organically links physical entities, algorithm decision nodes, and data flow links in the laboratory, forming a dynamic mapping model of multi-dimensional elements. By comparing traditional and unmanned laboratory processes, the inherent mechanism of risk points migrating from physical operations to virtual decision-making is revealed, providing underlying structural support for subsequent transparency and traceability.

[0021] Through in-depth analysis of all aspects of the laboratory, modules such as equipment, data, environmental status, algorithm models, control commands, sensor acquisition, and AI interpretation results are abstracted into ontological nodes. Each element is assigned a role and behavioral label based on attribute relationships. For example, equipment is associated with specific operating status and maintenance cycles; data nodes are mapped to quality labels such as acquisition / processing / storage; and algorithm entities are defined with their decision boundaries and traceability IDs. This comprehensive ontological system of laboratory elements is constructed using graph theory. As a formal basis, in which For the collection of all laboratory elements, A set of directed edges driven by interactions, dependencies, and processes between elements. Define the categories and attributes of each entity and relationship.

[0022] Building upon this foundation, the construction of a dynamic risk relationship graph becomes the core technological support. This process begins with the construction of entities and relationships within the knowledge graph, introducing causal reasoning and risk propagation mechanisms. In graph construction, firstly, based on predefined ontologies, input data such as raw process logs, equipment operation data, AI decision-making chains, and user operation sequences are parsed into triples, such as (AI interpretation model A, processing, sample data D), (detection device E, collected from, sensor S), (maintenance work order, trigger, maintenance action), etc. The resulting raw relational knowledge base is then standardized into a consistent expression through entity disambiguation, attribute alignment, and other methods. For risk modeling, the system introduces edge weights and causal directions, adopting a Bayesian network foundation, treating each risk event or potential anomaly node as a network node. Its relationship with other nodes (such as equipment malfunction leading to data anomalies, and data anomalies causing algorithm misjudgments) is described as directed edges, with edge weights. It represents the statistical strength of a certain abnormal causal transmission.

[0023] At the algorithmic level, the dynamic risk relationship graph is driven by a sliding window of time-series data, continuously extracting factual increments from process logs and anomaly detection modules to constantly supplement and iterate the risk causal chain. The following formalization process is adopted: For the graph... In version t, let the newly added fact set be... Every fact Representing the occurrence of triples at time point t, the upstream Bayesian structure learning algorithm detects the establishment of new nodes / edges and subsequent diffusion paths based on the conditional mutual information criterion. This process can be expressed as: for any anomalous node... Calculate each suspicious upstream node Boundary rights update For conditional mutual information, (This is the set of parent nodes). If the update threshold is met, a new causal chain is created / adjusted to reflect the dynamic changes in risk transmission as the process evolves.

[0024] At runtime, depth-first traversal (such as DFS / BFS) is used to quickly locate the risk propagation path after an anomaly occurs, supporting a complete link reconstruction of "why a device failure ultimately led to an error report". At the same time, each relationship edge and node update is accompanied by metadata (responsible person ID, operation signal strength, AI algorithm version number, device maintenance status, etc.), forming extremely detailed responsibility and process transparent labels, accompanied by full lifecycle records.

[0025] Based on this, when there is a need for result tracing or accountability review, there is no need to rely on subjective process backtracking. The algorithm can directly and efficiently reconstruct the complete lifecycle trajectory of "anomaly - data - algorithm - decision". The algorithm can also introduce graph embedding and clustering methods to detect anomaly patterns and aggregate high-risk nodes, thereby identifying risk clusters in advance and achieving multi-objective assurance of early risk warning, high process transparency, and clear allocation of responsibility.

[0026] Step Two: Accountability Retrospective Based on AI-Driven Decision-Making Transparency The AI ​​model's input features, decision-making logic, and output results are automatically and hierarchically recorded, supplemented by a natural language generation module to produce understandable reports that are easy to regulate and review. Through technology stack adaptation and a transparency level classification scheme, the interpretation of different decision-making stages can be adaptive, providing strong data evidence for regulation and risk control.

[0027] In intelligent, unmanned laboratory environments, the decision-making process of AI models is highly automated and complex. The traditional "black box" algorithm application model poses significant challenges to laboratory management, especially in quality control, incident tracing, and liability determination. Therefore, the entire interpretability chain needs structural enhancement. The implementation process encompasses deep correlation recording and semantic representation across three dimensions: input, process, and output, forming a transparent-box automated backtracking system.

[0028] like Figure 2 As shown, the design places great emphasis on transparency, generating adaptive explanation reports for each AI decision. In its implementation, the underlying architecture unfolds with a layered recording mechanism, covering the capture of original input features (…). The model's internal reasoning chain Result output ( It also synchronously encodes metadata such as the operating environment context and parameter status as extended records.

[0029] In the input feature tracking stage, all data is automatically processed before it flows into the AI ​​model. Hash tagging and versioning are performed for each feature variable. Not only are the original values ​​retained, but meta-features such as source, collection path, preprocessing history, and anomaly identification tags are also added in real time, formalizing them into a labeled set. Each element represents a feature, source, time, and associated metadata. This input chain archive provides a foundation for subsequent backtracking of input anomalies and accountability.

[0030] Entering the AI ​​model inference stage, the mechanism calls the model. The system automatically captures the internal state evolution, especially at key nodes such as feature extraction (e.g., activation of intermediate layers in deep networks) and decision path branches, inserting decision record vectors. Considering the need for tiered transparency, the system allows users or administrators to select between "shallow explanation" and "deep analysis" granularity. For example, at the shallow level, only input-output pairs can be recorded, while at the deep level, the state of each layer of the model is automatically recorded. Decision path p, and activation weights This part is mathematically described using the following chain: For any model decision process, construct an explanatory chain. L represents the number of levels, covering all data, operations, and control parameters from features to results.

[0031] The key anomaly lies in the highly nonlinear, black-box nature of the model's decision-making process. It's crucial to ensure that the interpretation is sensitive to risk points for each actual inference. This is addressed by employing a transparent box backpropagation algorithm, utilizing a backpropagation mechanism (BET) to extract information from the prediction results. In neural network or tree model structures, the algorithm iteratively backcalculates to the most influential input features and intermediate decision-making modules, automatically labeling "high-contribution" nodes. Formally, the results... Define its relationship to input features Importance score (i.e., first-order sensitivity analysis), while for multi-layer models, the influence of intermediate nodes is recursively accumulated to ultimately find the feature path that contributes the most to the current decision. For tree-like models, node branch paths are directly used as explanatory chains, and key decision segments are found through traversal.

[0032] After all the link data is automatically extracted and recorded in layers as described above, it is synchronously input into the semantic rendering generation module. Based on the templated natural language generation algorithm (NLG), the structured links are transformed into highly readable explanatory reports. For example, it can automatically generate a human-friendly explanation such as "On 2024-06-13 09:15, the algorithm model v2.4 triggered an anomaly judgment because the temperature and humidity of the batch of samples entering the warehouse exceeded the standard (feature x3=27.1). The decision was dominated by the intermediate layer node h7, with a weight exceeding the average by 2.3 times, contributing 76% to the final judgment." This can be used in actual business scenarios such as automatic archiving in laboratories, complaint review, and regulatory inspection.

[0033] To balance differentiated adaptation and automated operation and maintenance, a transparency-layered control module is introduced. This module uses predefined transparency levels. Automatically adjust the backtracking depth and interpretation granularity: such as The hierarchical report only states, "This judgment is based on model X and gives result Z for input Y." It details feature combinations, branch decisions, weight distribution, historical comparisons, and abnormal paths. The interpretation level can be controlled through algorithm parameter adjustments to balance operational efficiency and compliance transparency, in accordance with regulatory or auditing requirements.

[0034] Step 3: Source tracing and anomaly accountability based on multi-source data By combining multi-dimensional data paths from devices, databases, and external interfaces, a full-link data fingerprinting and anomaly attribution algorithm is proposed. By integrating blockchain technology with a hash-based traceability mechanism, a unique and immutable identity fingerprint can be established for every piece of key data, every device operation, and every AI decision-making invocation, enabling precise traceability of "who did what, when, and at what stage" throughout the entire chain. When data anomalies or AI decision-making deviations are detected, the algorithm automatically triggers an anomaly accountability workflow. Combined with original process records, this assists in locating responsibility and provides a chain of evidence for emergency response and liability determination.

[0035] The data flow in intelligent unmanned laboratories presents a complex multi-chain parallel structure, encompassing equipment acquisition, data storage, task processes, AI inference, and business interactions, making traceability and accountability a highly challenging systems engineering project. In this scenario, simple data logs or alarm chains are insufficient for accurate traceability. To dismantle information silos and reconstruct trust chains, this research introduces the organic integration of blockchain's immutable ledger and hash fingerprinting technology. This assigns a highly unique fingerprint to every piece of key data, equipment action, and AI decision, ensuring the authenticity and clarity of responsibility throughout the entire chain.

[0036] In practice, all data nodes involved in data collection, input, processing, transmission, and storage—whether data collection devices, database tables, AI decision services, or external interface calls—are uniformly encapsulated in a meta-transaction structure. , Includes a unique serial number (id), timestamp (ts), operation subject (actor), business type (op), data source (src), flow destination (dst), content digest (content), hash signature (hash), and preceding transaction hash (...). ) and secure signature (sig).

[0037] When each data action is generated, the algorithm uses multi-strategy hash compression on its key content blocks, specifically the SHA-256 algorithm: The "||" operator is used for string / binary concatenation. The blockchain structure is determined by each record... Fields naturally connect all links, forming a three-dimensional chain-like ledger. Every operation, process branch, and data change must consume a unique number on the chain, forming a strictly sequential chain.

[0038] Device operation logs are synchronized to the blockchain. For example, "Sensor X writes to platform Y, sample batch Z" is directly encapsulated into an on-chain block. Model versions, input summaries, and inference conclusions during AI inference are also formed into independent blocks and incorporated into the global traceability graph. Especially in external interface linkage scenarios, signature and ID mapping technology enables identity fusion between multi-source heterogeneous systems. Whether it's data blocks from local databases, cloud interface packages, or remote calls from AI modules, everything is unified into a single fingerprint block, uniquely identifying its link location and action metadata through hashing.

[0039] When data anomalies occur, the anomaly detection module (such as threshold anomaly detection based on statistics, rules, or AI) captures the deviation and triggers the accountability algorithm to initiate a closed-loop process. Starting from the latest anomaly record block, the algorithm traces back along the "account ledger" using the `prev_hash` field, recursively scanning each preceding key node to track the entire process, including original data entry, changes, algorithm judgment, and operator identity. The backtracking chain length is set to... , can The algorithm retrieves the root causes of anomalous attributes within the analysis window. To aid in accurate attribution, it employs conditional probability or Bayesian attribution factors based on the distribution of actor and operator types within the analysis window. Dynamically assess the attribution of responsibility. For example, entity A has actions on the critical link. The probability of causing an anomaly is By combining the signature traceability results on the blockchain, the system automatically determines the "most likely responsible entity / system / step". At the same time, the blockchain's multi-signature and permission verification mechanism ensures that each block is signed with the operator's local private key, and any subsequent modifications are immediately invalidated, giving all "who did what, when they did it, and what they did" irrefutable digital evidence.

[0040] If abnormal data affects terminal decisions or reports, the system automatically triggers secondary tasks, such as tracing the sealed sample batches, interrupting business links, and pushing a list of responsibilities to manual control units or regulatory departments. Relying on on-chain connections, the source tracing report can be exported with one click. The content includes multi-dimensional tags such as data anomaly points, key blocks in the link, operators in the process, AI model information, original input summaries, and final misjudgment responsibility suggestions, providing a strong chain of evidence for rapid accident response, accountability and compensation, and continuous improvement.

[0041] By grafting traditional data silos into a multi-dimensional and traceable business loop using digital blocks and encrypted signatures, whether it is simple data collection and storage, or cross-regional and cross-cloud device collaboration, AI model inference and optimization, all information flows can be "documented on the chain, traceable afterward, and unavoidable", which greatly reduces the problem of risk dispersion after human intervention or even conflicts of interest.

[0042] Step 4: Optimize the closed loop of elements, processes, and responsibilities. The process incorporates a multi-expert perspective responsibility allocation decision-making support algorithm: This algorithm integrates historical expert review data, scenario cases, and multiple weights to automatically suggest reasonable responsibility allocation weights and countermeasures, achieving regular iteration and closed-loop optimization at the theoretical, technical, and management levels, ensuring that the solution is both scientific and easy to implement.

[0043] When the risk prevention and traceability technology system for intelligent unmanned laboratories reaches its initial stage of development, relying solely on underlying engineering and mathematical mechanisms is far from sufficient to ensure that the system fully aligns with business scenarios and that the technical measures are highly adaptable to operational and regulatory realities. At this point, effectively integrating the knowledge and experience of industry authorities becomes crucial. Therefore, in optimizing the solution design, a composite review working group is formed, comprising experts from multiple disciplines, the technology implementation team, and regulators. A Delphi-like multi-round anonymous consultation and information feedback process is employed to "de-identify" and gather independent judgments, eliminating the influence of subjective power, and gradually reaching consensus through systematic, phased analysis. Laboratory management, AI / big data technology experts, and legal compliance and regulatory officials raise questions, supplement technical details, and verify scenario cases on key categories—such as the division of responsibility chains for AI errors, emergency closure loops for unconventional data anomalies, and the interpretability of on-chain attribution results. Subsequently, by statistically aggregating and distributing expert opinions, the focus of disputes is analyzed at multiple levels, gradually converging the consensus among all parties.

[0044] Based on historical expert judgments and actual case review data, the algorithm quantifies personal experience, event type preferences, weight differences, and case context factors to build a three-dimensional evaluation tensor of expert-scenario-solution.

[0045] Specifically, gathering experts For each abnormal event or system defect scenario to be analyzed, the entire process element chain is sorted out. (For example, equipment data collection anomalies, AI model making decisions outside its scope, data interface tampering), organize experts to make weighted judgments on the attribution of responsibility, emergency measures, and prevention mechanisms for each node. Original responsibility recommendations. By normalizing, the total score is guaranteed to be 1.

[0046] Furthermore, to overcome the review distortion caused by fluctuations in expert opinions and mismatched case scenarios, the algorithm introduces a sample weighting and historical precedent Bayesian parameter tuning mechanism. Each real case is represented by a feature vector. Encoding, such as (event context, equipment type, operator experience, AI model version, external data source credit rating, occurrence time, and preceding nodes), and consistency with experts' past opinions on this type of scenario. Make weighted adjustments. Here, the overall responsibility recommendation output is the aggregated weights of all experts. coefficient Determined by the fit between the current experts and the case context, the system can be automatically assigned values ​​based on Bayesian factors or historical KNN consistency indicators to ensure that the recommendations on responsibility are not dominated by extreme irrelevant biases.

[0047] When process nodes have a small overlap in their decision-making due to disputes, the system triggers a first-order fuzzy set weight allocation kernel—using a multi-element integration mechanism such as the Owa operator: parameter Determined by the distribution of expert preferences, this simulation of the diversity of responsibility-sharing decisions in reality effectively suppresses the influence of a few extreme judgments on the objectivity of the overall plan.

[0048] Example: The unmanned laboratory automatically conducts organic synthesis experiments, with all operations controlled by robots and AI algorithms. Key elements of the laboratory include reaction vessels, automatic sensors, temperature control systems, and AI decision-making models. In one experiment, a malfunction in the temperature control system led to abnormal results, necessitating accountability.

[0049] Step 1: Element Mapping and Risk Control Modeling The model abstracts each experimental element (equipment, data, environment, AI nodes, etc.) into ontology nodes and labels them with roles and behaviors.

[0050] Construct an experimental process relationship graph to link different elements in chronological order.

[0051] Element node table: Step 2: Modeling the Relationship Between Risk Control Links and Risk Graph A sliding window mechanism is used to collect data in real time and generate a risk causal relationship graph. For example: At 14:00, the temperature control system reported an abnormal temperature, the AI ​​model determined the abnormality, and the high temperature alarm triggered automatic shutdown.

[0052] Step 3: Transparency and Accountability in AI Decision-Making The system records input features (temperature, pressure), interpretation logic (judging abnormal temperature changes), and output results (automatic shutdown suggestions) in layers.

[0053] Example of natural language report output: "At 14:00:23 on June 5, 2024, the temperature control system S01 detected an abnormal temperature rise of 2.5℃ in the reactor E01; the AI ​​model D01 analyzed the data collected by sensor P01 and determined it to be abnormal, recommending immediate shutdown. Input features include temperature data (32.5℃), historical temperature curves, reliable data acquisition link, and anomaly tags have been archived." AI Decision Hierarchy Record Data Table: Step 4: Multi-source data tracing and anomaly accountability All critical operations and data structures are encapsulated into meta-transactions and uploaded to the blockchain.

[0054] Anomaly detection triggers the accountability process, tracing back the original data and the chain of responsibility, and confirming it with a blockchain signature.

[0055] Meta-transaction blockchain data table: Step 5: Responsibility Allocation and Closed-Loop Optimization The expert group assigns authority to key links and automatically suggests responsibility attribution and emergency measures.

[0056] After multidisciplinary review, the system automatically generates the final responsibility attribution and optimization suggestions: "The responsibility for the temperature control system's S01 malfunction accounts for 50%, the AI ​​model's D01 accounts for 30%, and the sensor's P01 accounts for 20%. It is recommended that the temperature control system strengthen its self-checking mechanism, optimize the AI's interpretation threshold, and regularly calibrate the sensors." Responsibility Weight Allocation Table: This embodiment demonstrates the process for tracing responsibility for anomalies in an unmanned laboratory. Through element modeling, link recording, blockchain traceability, AI transparent explanation, and expert closed-loop attribution, the entire process is digitized, automated, traceable, and auditable.

[0057] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0058] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or make equivalent substitutions for some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for AI decision-making transparency and accountability in an intelligent unmanned laboratory, characterized by: The method includes: Laboratory element mapping and risk control modeling: Construct an ontology system of key laboratory elements adapted to intelligent unmanned scenarios. Employ a dynamic risk relationship graph construction algorithm to organically link physical entities, algorithm decision nodes, and data flow links in the laboratory, forming a dynamic mapping model of multi-dimensional elements. Based on the accountability of AI decision-making transparency, the input features, decision-making logic process and output results of the AI ​​model are automatically recorded in layers, and supplemented by a natural language generation module to output understandable reports that are easy to supervise and review. Based on multi-source data tracing and anomaly accountability, blockchain technology is combined with hash tracing mechanism to establish a unique and tamper-proof identity fingerprint for every key data, every device operation, and every AI decision call. The element-process-responsibility closed-loop optimization adopts a responsibility allocation decision-making assistance algorithm with multiple expert perspectives. It integrates historical expert review data, scenario cases, and multiple weights to automatically suggest reasonable responsibility allocation weights and response measures.

2. The method for AI decision-making transparency and accountability in an intelligent unmanned laboratory according to claim 1, characterized in that: The aforementioned laboratory element mapping and risk control modeling includes: constructing a key laboratory element ontology system for intelligent unmanned scenarios, by abstracting modules such as equipment, data, environmental status, algorithm models, control commands, sensor acquisition, and AI interpretation results into ontology nodes, and assigning role labels and behavior tags to each node; Using a graph theory structure, nodes are sets of laboratory elements, edges are interaction dependencies or process-driven relationships between elements, and relations are entity attributes and categories. It parses multi-source input data such as process logs, equipment operation data, AI decision-making links, and user operation sequences to generate a standardized triple knowledge base, and ensures the standardization and uniformity of the ontology through entity disambiguation and attribute alignment.

3. The method for AI decision-making transparency and accountability in an intelligent unmanned laboratory according to claim 1, characterized in that: The accountability traceability based on AI decision-making transparency includes: automatically recording the input features, decision-making logic process, and output results of the AI ​​model in a hierarchical manner, and using a natural language generation module to output an explanatory report that can be supervised and reviewed; based on the hierarchical recording mechanism, synchronously encoding and archiving the original input features, model inference chain, result output, and operating environment metadata; and automatically archiving each feature and its source, collection link, pre-processing history, and anomaly identification label information in the input feature tracking stage to ensure the traceability of the data link. During the AI ​​model inference phase, internal state evolution, decision path branches, and activation weight information are captured, and the granularity and depth of the interpretation report are automatically controlled according to the transparency level classification scheme. All link data is transformed into structured natural language reports through the semantic rendering generation module, enabling a fully transparent interpretation of AI decision-making results and supporting automated support for input anomaly tracing, responsibility identification, and risk control.

4. The method for AI decision-making transparency and accountability in an intelligent unmanned laboratory according to claim 1, characterized in that: The aforementioned multi-source data traceability and anomaly attribution mechanism includes: employing a full-link data fingerprinting and anomaly attribution algorithm, integrating blockchain technology with a hash traceability mechanism, establishing a unique and tamper-proof identity fingerprint for each key data item, each device operation, and AI decision call, and uniformly encapsulating all data collection, entry, processing, transmission, and storage nodes into a meta-transaction structure, including a unique sequence number, timestamp, operation subject, business type, data source, flow destination, content summary, hash signature, preceding transaction hash, and security signature. Each data action forms a blockchain structure through a multi-strategy hash compression algorithm, achieving sequential on-chain connection. Device operation logs and AI inference records are all uploaded to the blockchain simultaneously. External interface calls are uniformly incorporated into the global traceability map through signatures and numbers, ensuring identity fusion and unique action identification across the entire chain.

5. The method for AI decision-making transparency and accountability in an intelligent unmanned laboratory according to claim 1, characterized in that: The aforementioned element-process-responsibility closed-loop optimization includes: introducing a responsibility allocation decision-making assistance algorithm from multiple expert perspectives, combining historical expert review data, scenario cases, and multi-party weights to automatically suggest reasonable responsibility allocation weights and corresponding measures, covering theoretical, technical, and management-level iterations and closed-loop optimization; Through multiple rounds of anonymous consultation and information feedback processes, and by integrating multidisciplinary review by laboratory management, AI technology experts, legal compliance and regulatory officials, collaborative judgments are made on the allocation of responsibility chains for key process nodes, the closed-loop emergency response for anomalies, and the interpretability of on-chain attribution results.

6. The method for AI decision-making transparency and accountability in an intelligent unmanned laboratory according to claim 2, characterized in that: The aforementioned laboratory element mapping and risk control modeling includes: based on a time-series data sliding window mechanism, dynamically and continuously supplementing and iteratively updating the causal relationship of risks, realizing automatic chain building of causal transmission of abnormal nodes, generating a dynamic risk relationship map covering the entire process, and realizing data marking and transparency of responsibility processes for the entire life cycle of key laboratory elements.

7. The method for AI decision-making transparency and accountability in an intelligent unmanned laboratory according to claim 4, characterized in that: The aforementioned multi-source data tracing and anomaly accountability mechanism detects data anomalies or AI decision-making biases by capturing deviation points in the anomaly detection module. This automatically triggers the anomaly accountability workflow, tracing back the original data and responsibility chain along the blockchain. Combined with signature tracing and attribution determination on the blockchain, it assists in accurately locating the attribution of responsibility, providing full-process digital evidence protection for accident emergency response and responsibility determination.

8. A method for AI decision-making transparency and accountability in an intelligent unmanned laboratory according to claim 5, characterized in that: The aforementioned element-process-responsibility closed-loop optimization includes: algorithm-based expert gathering, organizing the proposal of weights for responsibility attribution and emergency measures for each element chain node, and achieving dynamic optimization of responsibility allocation proposals through normalization, sample weighting, and historical case parameter tuning mechanisms.

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