Precomputed Explanation Scores for AI Model Interpretability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional explainability techniques for machine learning models are computationally intensive and resource-heavy, making real-time explanation generation impractical, especially when moving from development to production systems, and often rely on assumptions that limit accuracy due to symmetrical classification boundaries and monotonically constrained response functions.
Innovation Solution
Generating precomputed explanations based on input features of transactions using an explainability model trained on historical data, allowing for the selection of unified explainability scores from precomputed explanations for new transactions, reducing computational resources needed and enabling real-time or near-real-time explanations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional explainability techniques are used to generate explanations for machine learning model outputs, then explanation accuracy is maintained, but computational resources and processing time are excessively high making real-time generation impractical
Solution Approach 1:
The patent precomputes explanations for historical transactions during offline processing and stores them in a database. When a new transaction arrives, the system retrieves precomputed explanations that match the transaction's input features, avoiding the need for real-time computation. This preliminary action resolves the contradiction by shifting computational work from real-time to offline, maintaining accuracy while enabling rapid explanation delivery.
Solution Approach 2:
The patent creates copies of explanations from similar historical transactions and applies them to new transactions. Instead of computing explanations from scratch for each transaction, the system identifies transactions with similar input features and reuses their precomputed explanations. This copying approach maintains explanation quality while dramatically reducing computational resources and time requirements.
2Loss of information
If conventional explainability techniques are deployed in production systems, then model interpretability is achieved, but system resource consumption becomes unsustainable
Solution Approach 1:
The system performs explainability computation in advance during offline batch processing of historical transactions, storing results in a database. During production, it only retrieves and matches precomputed explanations, reducing online computational load from intensive to minimal operations. This resolves the contradiction by maintaining full interpretability capability while reducing real-time resource consumption to sustainable levels.
Solution Approach 2:
The patent segments the explainability task into offline precomputation phase and online retrieval phase. The computationally intensive explanation generation is separated from the real-time transaction processing, allowing resource-intensive operations to be performed when resources are abundant (offline) and lightweight operations to handle real-time requests. This segmentation makes production deployment sustainable.
3Loss of time
If real-time explanation generation is implemented using conventional methods, then up-to-date explanations are provided, but computational complexity makes the system infeasible
Solution Approach 1:
The system copies precomputed explanations from the database that match new transaction features, avoiding complex real-time computation. By identifying transactions with similar input feature patterns and reusing their explanations, the system delivers immediate explanations without the computational complexity of conventional real-time methods, resolving the contradiction between speed and complexity.
Data Source
AI summary
A method, system, and computer program product generate precomputed explanation scores in AI systems. The method includes obtaining a set of labeled transactions comprising input features and corresponding output labels generated by a machine learning (ML) model and generating an explainable artificial intelligence (XAI) module. The generating includes clustering the labeled transactions based on the input features, scoring homogeneity of the clustered transactions based on the corresponding output labels, and selecting at least one cluster from the clustered transactions based on the homogeneity scoring. The generating further includes obtaining, by an explainability model, explainability scores for transactions in the at least one cluster, generating a unified explainability score for the at least one cluster based on the explainability scores, and storing the unified explainability score in a set of precomputed explanations.