Data Processing Failure Prediction with Explainable AI
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Inference models used for predicting component failures in data processing systems lack transparency, making it difficult for downstream consumers to trust the generated predictions without manual validation, which is time-consuming and inefficient.
Innovation Solution
Implementing explainable AI techniques to extract hidden knowledge from inference models, store it as structured knowledge attributes, and provide it through a repository for downstream consumers, along with interactive AI chatbots for easier understanding and personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inference models are used for predicting component failures, then prediction capability is improved, but transparency and trustworthiness deteriorate
Solution Approach 1:
The patent introduces an explanation generation module that acts as an intermediary between the inference model and downstream consumers. This module generates human-understandable explanations for model predictions, bridging the gap between the black-box model and the need for transparency. The explanations include feature importance scores, prediction confidence levels, and rationale for predictions, allowing consumers to trust and validate predictions without manually examining the complex model internals.
2Reliability
If manual validation of predictions is performed, then trustworthiness is improved, but time consumption and efficiency deteriorate
Solution Approach 1:
The system performs preliminary validation by automatically generating explanations and confidence scores for predictions before they are presented to downstream consumers. The explanation generation module pre-computes feature importance, prediction rationale, and confidence metrics, so that consumers receive pre-validated, explained predictions rather than raw model outputs requiring manual scrutiny. This preliminary explanation generation significantly reduces the time consumers need to invest in validating predictions.
3Loss of information
If explainable AI techniques are implemented, then transparency is improved, but system complexity deteriorates
Solution Approach 1:
The patent segments the explainable AI functionality into distinct modular components: an explanation generation module, a structured knowledge repository, and integration interfaces. The explanation generation module is further divided into sub-components for different explanation types (feature importance, prediction rationale, confidence scoring). This segmentation allows each component to be developed, validated, and maintained independently, reducing overall system complexity despite the added transparency capabilities.
4Loss of information
If structured knowledge repository is implemented, then information organization is improved, but storage and retrieval complexity deteriorates
Solution Approach 1:
The structured knowledge repository transforms unstructured model explanation data into organized structured knowledge using standardized parameters and schemas. Explanations are converted into standardized formats with defined fields for prediction outcomes, feature importances, confidence levels, and rationales. This parameter-based structuring enables efficient querying and retrieval while maintaining organization, as the standardized schema allows for consistent data access patterns and reduces the complexity of managing diverse explanation formats.
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.