System for reliability assessment of artificial intelligence models

Abstract

System for the reliability assessment of artificial intelligence models, comprising: a model interface configured to receive input data, output data, and metadata associated with the output data of at least one artificial intelligence model; a perturbation module configured to generate multiple modified input instances from a single input instance; a consistency analysis module configured to compare the output data of the input instance with the output data of the modified input instances and determine a perturbation stability measure; a context assessment module configured to determine a context compatibility measure based on deployment context data; an evaluation module configured to generate a reliability score for the output data from at least the perturbation stability measure and the context compatibility measure; and an output control module.which is set up to assign the output data to one of several reliability states depending on the reliability value.

Description

Technical field The invention relates to a computer-implemented technical system for evaluating the reliability of outputs from an artificial intelligence model. In particular, the invention relates to a system comprising a model interface, perturbation generation, consistency analysis, context evaluation, evaluation logic, and output control for determining a reliability state for output data generated by an artificial intelligence model on a case-by-case basis. State of the art Artificial intelligence models are increasingly used in technical, industrial, administrative, and decision support applications. In established systems, the performance of such models is typically evaluated using global measures of goodness of fit, such as accuracy, confidence, robustness, or benchmark performance. However, such evaluation approaches are regularly model-wide or dataset-wide and do not allow for a sufficiently differentiated assessment of the reliability of a specific individual output under a particular application context. Furthermore, it is known from the prior art to output additional uncertainty values, confidence values, or explanatory data. However, such known measures often only provide isolated partial information. In particular, a comprehensive technical architecture is frequently lacking that integrates the stability of a specific model output against controlled input variations, the consistency between output data and associated metadata, and the context-compatibility of the output data in an operational environment, and derives a directly usable reliability state from this. Furthermore, existing solutions suffer from the problem that while artificial intelligence output can be provided with high confidence, even minor, semantically insignificant variations in the input data can cause it to become inaccurate or unsuitable for a specific application context. Therefore, relying solely on confidence can lead to technically inadequate or error-prone decisions. Therefore, there is a need for a technical system that not only assesses general model quality, but also determines the reliability of a specific model output on a case-by-case basis, context-dependently and in real time or quasi-real time, and derives a technical control decision from this. Object of the invention The invention is based on the objective of providing a system for the reliability assessment of artificial intelligence models, with which a reliable reliability value and / or reliability state can be generated for a specific model output, taking into account controlled input variations and context-related deployment data. In particular, a system should be created that combines the stability of a model output, its context compatibility and optionally further metadata in a technical evaluation architecture and enables downstream technical output control. Summary of the invention The present invention provides a system for evaluating the reliability of artificial intelligence models, comprising: a model interface for receiving input data, output data, and associated metadata of at least one artificial intelligence model; a perturbation module for generating modified input instances from an input instance; a consistency analysis module for determining a stability metric based on the output data of the input instance and the output data of the modified input instances; a context evaluation module for determining a context compatibility metric based on data from the deployment context; an evaluation module for generating a reliability score by combining at least the stability metric and the context compatibility metric; and an output control module for assigning the output data to a predetermined reliability state. This ensures that reliability is not derived solely from a static model property, but rather from the behavior of the specific model output in response to targeted variations, as well as from its suitability for the respective application context. Consequently, the system can trigger subsequent technical control measures – such as enabling, restricting, escalating for human review, or rejecting an output. In preferred embodiments, the modified input instances are generated as semantically restricted variations of the input instance. Even more preferably, explanatory data, uncertainty data, data on historical behavior, or source data are additionally included in the reliability assessment. Furthermore, a calibration module can be provided to adjust thresholds or weighting parameters based on result data from previously evaluated outputs. Detailed description of the invention The present invention relates to a system for the reliability assessment of artificial intelligence models, with which the reliability of an output generated by an artificial intelligence model is determined not only on the basis of an isolated confidence value, but on the basis of several technically acquired and processed evaluation parameters. The invention is particularly suitable for applications in which outputs of artificial intelligence are to be used, further processed, released, restricted, or rejected in a specific operational context. Familiar systems for evaluating artificial intelligence models often rely on global quality measures such as accuracy, loss functions, benchmark results, or simple confidence levels for individual predictions. Such approaches are insufficient in many practical applications because they do not provide a reliable indication of whether a specific, current model output is sufficiently reliable under the actual operating conditions. For example, a model output may be unsuitable despite high confidence if it becomes unstable under minor input changes, if its explanation does not match its actual output, or if it is inadmissible or technically unsuitable in a given application context. The invention therefore provides a technical system that determines the reliability of a specific model output on a case-by-case basis and derives a technically usable reliability state from this. In a preferred embodiment, the system comprises a model interface, a perturbation module, a consistency analysis module, a context evaluation module, an evaluation module, an output control module, and optionally a calibration module. The model interface serves to receive input data, output data, and associated metadata from at least one artificial intelligence model. Input data can include, for example, text data, image data, sensor data, measurement data, control data, tabular data, feature vectors, or combinations thereof. Output data can include classification outputs, regression values, generated text, image segments, control commands, recommendations for action, prioritization results, or other results generated by the artificial intelligence model. Metadata can include, in particular, confidence data, uncertainty data, explanatory data, source information, log data, model version data, historical behavioral data, or context identifiers. In a preferred embodiment, the model interface processes the received data in a standardized format and passes it on to the subsequent functional modules. The model interface can perform data normalization, format standardization, pre-validation, and identification assignment. In particular, the model interface can assign each received input instance a unique instance identifier, ensuring that all associated perturbations, analysis results, context data, and evaluation results remain related to the same input instance. A key element of the invention is the perturbation module. This module is designed to generate multiple modified input instances from an original input instance. The modified input instances are preferably semantically limited variations of the original input instance. This means that the variations are chosen to preserve the essential content or technical meaning of the input instance, while introducing minor changes in form, presentation, sequence, weighting, noise, wording, scaling, or feature values. For text data, these changes might include, for example, rewording, synonym substitution, modification of non-essential sentence elements, or variations in the order of equivalent information units. For image data, these changes might include brightness adjustments, slight shifts, cropping, contrast variations, or other content-preserving transformations.With structured data, individual non-critical values ​​can be changed within predefined tolerance ranges, or the order of non-causally relevant fields can be varied. This generation of modified input instances creates a variation space around the original input instance. The invention uses this variation space to evaluate not only the output to the original input instance, but also the model's behavior under controlled, limited changes. This allows it to determine whether the model's output is stable, fragile, inconsistent, or context-dependent. The modified input instances generated by the perturbation module are fed back into the artificial intelligence model. The resulting output data, along with the original output, is provided to the consistency analysis module. The consistency analysis module compares the output of the original input instance with the outputs of the modified input instances. Depending on the type of model and the outputs, the consistency analysis module can apply different comparison metrics. For classification models, it can check whether the same class is retained or whether class changes occur. For regression models, it can check how much the numerical target value fluctuates. For generative models, it can check whether key content-related statements, structures, or core semantic statements are preserved. A perturbation stability measure is determined from these comparisons. The perturbation stability measure indicates how robust the specific model output is to controlled, content-preserving input variations. A high perturbation stability measure means that the output or its essential properties are largely preserved despite variation. A low perturbation stability measure means that the output becomes unstable even with minor changes. This measure is an essential technical criterion for reliability assessment because it describes the case-by-case robustness of the output. In addition, the invention includes a context assessment module. This module determines a context compatibility metric based on operational context data. The operational context data can include, in particular, information about the intended purpose, the technical domain, the criticality of the application, user roles, approval levels, data sources, operating states, security profiles, geographical requirements, organizational rules, or regulatory specifications. The context assessment module evaluates whether the specific output of the model is technically permissible, appropriate, sufficiently secure, or only conditionally usable in the current operational context. In a preferred embodiment, the context evaluation module checks, in particular, whether there are contradictions, incompatibilities, or increased risk conditions between the model output and the application context. Thus, an output might be considered usable in a non-critical application, but subject to a stricter evaluation in a safety- or liability-relevant application. The invention therefore allows the same model output to be evaluated differently in different application contexts. In a further preferred configuration, the system can additionally incorporate explanatory data into the evaluation. Such explanatory data can include, for example, attribution scores, feature importance, textual justifications, saliency maps, decision logs, or internal model hints. The system can determine a congruence measure between the actual output data and the explanatory data. This verifies whether the explanation provided by the model is internally consistent with the output or whether there are contradictions between the explanation and the result. High congruence indicates greater plausibility of the output, while low congruence may suggest lower reliability. In another embodiment, the system can incorporate historical behavioral data from the same or a comparable model. This involves analyzing how the model has behaved in the past with similar input instances and whether the current output fits into a stable historical behavioral pattern. This allows for an additional temporal stability analysis. The parameters provided by the aforementioned modules are fed to the evaluation module. The evaluation module uses at least the perturbation stability parameter and the context compatibility parameter to calculate a reliability score for the specific model output. In preferred embodiments, the congruence parameter between output and explanation, confidence data, uncertainty data, historical behavioral data, or source weights are also incorporated into the reliability score. The evaluation module can use weighting factors, thresholds, rule sets, decision functions, fusion logics, or multi-level evaluation schemes for this purpose. The reliability score can be expressed as a scalar numerical value, as a vector of several sub-values, or as a structured evaluation representation. In a particularly preferred embodiment, the reliability score is described by several sub-components, in particular a stability component, a context component, a congruence component, and optionally a history component. These sub-components can be reported individually or combined into a single overall score. Another essential component of the invention is the output control module. This module is configured to assign the output data generated by the artificial intelligence model to one of several reliability states, depending on the determined reliability value. In a preferred embodiment, at least one autonomous operating state, one restricted operating state, one test state with human verification, and one rejection state are provided. If an output is assigned an autonomous usage state, it can be reused or executed in a downstream system without additional checks. If a restricted usage state is assigned, the output can only be used under additional conditions, to a limited extent, or in conjunction with technical control mechanisms. If an audit state is assigned, human review or approval is required before the output can be used further. If a rejection state is assigned, the output is not released and can be locked, discarded, or replaced by an alternative process. This output control makes the reliability assessment directly effective from a technical perspective. The invention is therefore not limited to a purely analytical evaluation, but generates a technical control decision that influences the actual further processing or execution of the model output. In a further preferred embodiment, the system includes a calibration module. The calibration module receives results data on previously evaluated outputs, in particular data on whether a released or restricted output has subsequently proven to be accurate, suitable, faulty, or problematic. Based on these results data, the calibration module can adjust at least one threshold and / or at least one weighting parameter of the evaluation module. In this way, the system can learn from real-world results without requiring the underlying artificial intelligence model to be completely retrained. Instead, the reliability evaluation layer is calibrated and adapted to the actual operating conditions. In a preferred technical implementation, the system is implemented on a computing unit with at least one processor and at least one memory. The individual modules can be designed as software-based functional blocks, containerized services, distributed components, or hardware-supported submodules. The model interface can be connected to one or more artificial intelligence models via standardized programming interfaces. The system can be operated locally, in a distributed manner, at the edge, in the cloud, or in a hybrid configuration. In a first exemplary application, a text model is fed with text input. The model generates a textual output along with confidence and explanatory data. The perturbation module generates several content-preserving reformulations of the text input. The model processes these variations again. The consistency analysis module determines whether the essential statements of the respective outputs remain the same or differ significantly. The context evaluation module considers whether the output is intended for use in an advisory, documentation, or decision-preparation context. The evaluation module calculates a reliability score based on this information. The output control module then decides whether the output is automatically forwarded, used only with a warning, submitted to a case worker for review, or rejected entirely. In a second example application, an image classification model is fed with image data. The perturbation module generates slight variations in brightness and cropping. The consistency analysis module checks whether the predicted class remains stable despite these variations. The context evaluation module considers whether the output is intended for statistical analysis only or as the basis for further technical action. Here, too, a reliability score is calculated and a reliability state is assigned. The invention is not limited to the examples shown. Rather, it can be used wherever the output of an artificial intelligence model is to be evaluated not only according to internal model metrics, but also according to its actual reliability in individual cases. The invention is particularly suitable for technical assistance systems, digital decision support, industrial control preparation, automated document evaluation, quality control, safety-relevant preliminary testing, rule-based release systems, and hybrid human-machine decision-making processes. A key advantage of the invention lies in the fact that it determines not only the abstract quality of a model, but also the reliability of a specific output in a concrete context. A further advantage is that the evaluation is technically objectified through the controlled generation and analysis of modified input instances. Another advantage lies in the technical integration of the reliability evaluation with downstream output control. Furthermore, the optional calibration module enables continuous adaptation of the evaluation logic to real-world results, thus allowing for application-oriented optimization of the overall system. The invention thus creates a technical system architecture with which the reliability of artificial intelligence outputs can be determined in a robust, case-specific and context-sensitive manner and implemented in technical control decisions.

Claims

System for the reliability assessment of artificial intelligence models, comprising: a model interface configured to receive input data, output data, and metadata associated with the output data of at least one artificial intelligence model; a perturbation module configured to generate multiple modified input instances from a single input instance; a consistency analysis module configured to compare the output data of the input instance with the output data of the modified input instances and determine a perturbation stability measure; a context assessment module configured to determine a context compatibility measure based on deployment context data; an evaluation module configured to generate a reliability score for the output data from at least the perturbation stability measure and the context compatibility measure; and an output control module.which is set up to assign the output data to one of several reliability states depending on the reliability value. System according to claim 1, characterized in that the perturbation module is configured to generate the modified input instances as semantically limited variations of the input instance, such that a content-preserving variation space surrounding the input instance is formed, and that the consistency analysis module is configured to determine the perturbation stability parameter based on changes in the output data within the variation space. System according to claim 1 or 2, characterized in that the metadata includes at least one from the group comprising confidence data, explanatory data, uncertainty data, historical behavioral data and source data, and that the evaluation module is configured to further determine the reliability value as a function of a congruence measure between the output data and the explanatory data. System according to claim 1, wherein the output control module is configured to selectively assign the output data to an autonomous usage state, a restricted usage state, a test state with human review, or a rejection state. System according to claim 1, characterized in that a calibration module is provided which is configured to receive result data for previously evaluated output data and to adjust at least one threshold value and / or at least one weighting parameter of the evaluation module depending on the result data.