Generative AI Explanation Framework With Contestable Attribution
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Solution Overview
Problem
Current Explainable AI (XAI) techniques fail to combine multiple attribution methods to generate explanations that are faithful, plausible, consistent, and sufficient, lacking contestability features necessary for high-stakes decision-making environments.
Innovation Solution
An integrated XAI framework that synthesizes Context, Model, and Data Attribution methods to generate holistic explanations, incorporating an aggregator controller to resolve conflicts and ensure faithfulness, plausibility, consistency, and sufficiency, with contestability features allowing users to accept or challenge model outputs and provide remediation pathways.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple attribution methods (Context, Model, and Data Attribution) are combined to generate comprehensive explanations, then the faithfulness and sufficiency of explanations are improved, but the device complexity and computational resources required increase
Solution Approach 1:
The patent combines three distinct attribution methods (Context Attribution, Model Attribution, and Data Attribution) into a unified XAI framework. Each method addresses different aspects of model decision-making, and their integration provides comprehensive explanations that are more faithful to the model's actual reasoning process while maintaining systematic management through an aggregator controller.
Solution Approach 2:
The patent introduces an aggregator controller as an intermediary component that receives outputs from multiple attribution methods, resolves conflicts between them, and synthesizes a unified explanation. This mediator manages the complexity of combining multiple attribution approaches while ensuring the final explanation maintains faithfulness, plausibility, consistency, and sufficiency.
2Loss of information
If multiple attribution methods are integrated to provide comprehensive explanations, then the sufficiency and plausibility of explanations are improved, but the computational time and resources increase
Solution Approach 1:
The patent performs preliminary actions by conducting context attribution first (analyzing input-output relationships), followed by model attribution (analyzing internal mechanisms), and finally data attribution (tracing training data influence). This sequential preliminary analysis of different attribution dimensions enables comprehensive explanations while managing computational resources through structured processing.
Solution Approach 2:
The patent implements feedback mechanisms where the aggregator controller evaluates the outputs from multiple attribution methods, resolves conflicts, and synthesizes a unified explanation. The feedback loop ensures that the final explanation satisfies all four properties (faithfulness, plausibility, consistency, sufficiency) while optimizing computational efficiency through iterative refinement.
3Reliability
If the XAI framework includes contestability features allowing users to challenge model outputs, then the accountability and trust are improved, but the ease of operation decreases
Solution Approach 1:
The patent segments the contestability feature into distinct functional components: (1) explanation generation from multiple attribution methods, (2) user interface for viewing explanations and submitting challenges, (3) challenge processing and remediation pathways. This segmentation allows users to interact with the system in a structured manner, improving ease of operation while maintaining accountability through comprehensive contestability.
Data Source
AI summary
A system and method for explaining and contesting outcomes of AI models, such as generative AI models, provides a desired explanation with desired explanation properties and provides a contestability feature. The system and method may also provide a remediation process when an AI model response is contested.