Failure Response Generation Using Explainable Prediction Knowledge
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Solution Overview
Problem
Existing inference models for predicting device failures lack transparency and trustworthiness, making it difficult for downstream consumers to rely on their predictions without manual validation, which is time-consuming and inefficient.
Innovation Solution
Implementing explainable AI to extract hidden knowledge from inference models, convert it into structured knowledge attributes, and generate failure prediction prompts to improve interpretability and trustworthiness, using large language models for customizable human-readable reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If inference models are used to predict device failures, then 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 hidden knowledge from the model's internal representations and transforms it into human-interpretable explanations, thereby maintaining prediction accuracy while improving transparency and trustworthiness
Solution Approach 2:
The patent replaces traditional black-box inference mechanisms with an explainable AI system that uses knowledge extraction and transformation processes. Instead of relying on opaque model outputs, the system substitutes a transparent knowledge representation and explanation generation mechanism that reveals the reasoning behind predictions
2Reliability
If manual validation of predictions is performed, then trustworthiness is improved, but time consumption increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate explanations and validate its own predictions through extracted hidden knowledge. The explainable AI layer autonomously produces interpretable outputs that demonstrate prediction rationale, eliminating the need for manual validation while maintaining trustworthiness
Solution Approach 2:
The patent performs preliminary action by extracting and organizing hidden knowledge from the inference model in advance. This pre-extracted knowledge is stored and readily available to provide immediate explanations when predictions are made, avoiding the time-consuming process of manual validation while ensuring trustworthiness
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 stored in a repository and may be provided for downstream use in order to increase the likelihood of preventing and/or mitigating future data processing system failures.