AI Explainability Refinement via Automated Diagnostic Analysis
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Solution Overview
Problem
Current explainability data for AI and ML models, particularly for blackbox deep learning models, is limited in providing interpretable results, especially for time series data, and often assumes human consumption, lacking automated refinement and remediation capabilities.
Innovation Solution
A system comprising a processor and memory that identifies probable causes of predicted events using explainability data, executes diagnostic analyses, and recommends remediation actions automatically, enriching explainability data and validating event probabilities without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If explainability data is generated for AI/ML models, then interpretability of model outputs is improved, but automation capability is worsened (human intervention required)
Solution Approach 1:
The system enables self-service by allowing the AI/ML model to automatically refine its own predictions using diagnostic analyses. The interpreter component automatically identifies probable causes, executes diagnostic analyses, and refines predictions without requiring human intervention, thus achieving both interpretability and automation.
Solution Approach 2:
The system implements feedback mechanisms where diagnostic analyses provide feedback to the prediction refinement process. The enricher component uses diagnostic data to update and refine the original prediction, creating a closed-loop system that continuously improves accuracy while maintaining automation.
2Measurement precision
If diagnostic analyses are executed to enrich explainability data, then accuracy of predicted events is improved, but device complexity is worsened
Solution Approach 1:
The system segments the prediction refinement process into distinct components: an interpreter component that identifies probable causes, an enricher component that executes diagnostic analyses, and a recommender component that provides remediation actions. This modular segmentation manages complexity while improving accuracy.
Solution Approach 2:
The system introduces an intermediary diagnostic analysis layer between the AI/ML model and the final prediction. This intermediary component (the enricher component) processes diagnostic data and feeds it back to refine the prediction, thereby improving accuracy without directly increasing the core model complexity.
3Productivity
If automated remediation actions are recommended, then productivity is improved, but reliability is worsened (lack of human verification)
Solution Approach 1:
The recommender component provides automated remediation actions based on the refined predictions and diagnostic analyses. The system serves itself by automatically generating and recommending actions without requiring human verification, thus improving productivity while maintaining reliability through robust automated reasoning.
Data Source
AI summary
Systems, computer-implemented methods, and computer program products that can facilitate refinement of a predicted event based on explainability data are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interpreter component that identifies a probable cause of a predicted event based on explainability data. The computer executable components can further comprise an enrichment component that executes a diagnostic analysis based on the probable cause.