ML Explanation Generation for Audience-Specific Interpretability
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Solution Overview
Problem
Machine learning models operate as black boxes, making it difficult to provide interpretable explanations that cater to different audiences with varying levels of technical expertise and interest, leading to ethical and human understanding issues.
Innovation Solution
A second machine learning model is trained to generate tailored, human-understandable explanations of a first machine learning model, considering interpretation criteria and domain-specific training data, enabling customization for different target audiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to perform tasks, then productivity and automation are improved, but interpretability and understanding of decision-making processes deteriorate
Solution Approach 1:
The patent introduces an explanation generation model as an intermediary component that translates the black-box outputs of machine learning models into human-understandable explanations. This mediator preserves the automation benefits while restoring interpretability by generating natural language explanations that describe the reasoning behind model predictions, thus resolving the contradiction between productivity and information loss.
2Device complexity
If machine learning models operate as black boxes, then device complexity is reduced, but ease of operation and ethical understanding deteriorate
Solution Approach 1:
The patent implements a self-service mechanism where the machine learning system automatically generates its own explanations without requiring external intervention. The explanation generation model is integrated into the system and autonomously produces interpretability outputs, making the black-box model self-explanatory and thus improving ease of operation while maintaining model structure simplicity.
3Productivity
If generic explanations are provided for machine learning models, then productivity is improved, but adaptability to different audiences deteriorates
Solution Approach 1:
The patent implements dynamic adaptability by making the explanation generation process responsive to audience characteristics. The system dynamically adjusts explanation content, detail level, and language based on the target audience's expertise and needs, transforming static generic explanations into dynamic customized ones while maintaining generation efficiency through automated processing.
Data Source
AI summary
A processing system including at least one processor may obtain description information of a first machine learning model, obtain a set of interpretation criteria for the first machine learning model, and generate, via a second machine learning model, an explanation text providing an interpretation of the first machine learning model in accordance with the set of interpretation criteria and the description information of the first machine learning model.