Interactive Data Processing Failure Management with Explainable AI
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Solution Overview
Problem
Inference models used to predict component failures in data processing systems lack transparency, making it difficult for downstream consumers to trust their predictions without manual validation, which is time-consuming and inefficient.
Innovation Solution
Implementing explainable AI techniques to extract hidden knowledge from inference models, storing it as structured knowledge attributes in a repository, and providing interactive AI chatbots to enhance interpretability and trustworthiness of failure predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If inference models are used to predict component failures, then failure prediction capability is improved, but transparency and trustworthiness deteriorate
Solution Approach 1:
The patent introduces an explainable AI layer as an intermediary between the inference model and end users. This layer extracts and presents structured knowledge attributes (such as feature importances, decision paths, and confidence scores) that mediate the information gap, allowing users to understand model predictions without compromising the model's predictive accuracy
Solution Approach 2:
The patent segments the inference model's internal knowledge into distinct, interpretable components such as feature importances, decision rules, and confidence metrics. By dividing the complex model output into manageable structured knowledge attributes, users can selectively examine specific aspects of the prediction process that build their trust
2Reliability
If manual validation of predictions is performed, then trustworthiness is improved, but time consumption and efficiency deteriorate
Solution Approach 1:
The patent performs preliminary extraction and organization of structured knowledge attributes from the inference model before user review. By pre-processing and structuring the explanatory information in advance, the system reduces the time users need to spend on validation while maintaining comprehensive transparency
Solution Approach 2:
The system enables users to independently validate predictions by providing them with structured knowledge attributes that allow self-assessment of model reliability. Users can examine feature importances and decision paths themselves without requiring time-consuming manual verification by experts
3Loss of information
If structured knowledge attributes are extracted and stored, then interpretability is improved, but system complexity deteriorates
Solution Approach 1:
The patent extracts structured knowledge attributes from the inference model and stores them in a separate knowledge repository. By taking out the explanatory information from the complex model structure and placing it in an external, organized repository, the system improves interpretability while managing complexity through separation of concerns
4Reliability
If interactive AI chatbots are implemented, then user confidence is improved, but implementation complexity deteriorates
Solution Approach 1:
The interactive AI chatbot serves as an intermediary that translates complex structured knowledge attributes into user-friendly explanations and answers. It mediates between the technical complexity of the inference model and user comprehension needs, building confidence through natural interaction without requiring users to understand underlying system complexity
Data Source
AI summary
Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and/or software components. Inference models may be implemented to predict future system infrastructure outcomes (e.g., component failures) using information recorded in logs that reflect the operation of the components. However, the models may be complex “black boxes” and may generate critical outcome predictions for downstream consumers without explanations of how the predictions are determined, resulting in downstream consumers having low confidence in the predictions. Therefore, hidden knowledge (e.g., structured knowledge attributes) of the models may be extracted and/or used to understand the underlying processes that the models use to predict the system infrastructure outcomes. The hidden knowledge may be provided for interactively managing data processing system(s) failures in order to increase the likelihood of preventing and/or mitigating future data processing system failures.