ML Model Explanation Generation for Audience-Specific Interpretability
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Solution Overview
Problem
Machine learning models (MLMs) operate as black boxes, leading to ethical, design, and human understanding issues due to their complexity, and existing interpretability techniques fail to provide tailored explanations suitable for diverse audiences with varying levels of detail and expertise.
Innovation Solution
A second machine learning model is trained to generate human-understandable explanations of a first MLM, customized for specific target audiences by using interpretation criteria and description information, allowing for tailored explanations based on user preferences and domain-specific training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are made more complex to improve prediction accuracy, then model performance is improved, but model interpretability deteriorates
Solution Approach 1:
A second machine learning model is introduced as an intermediary to translate the decisions of the first complex model into human-understandable explanations. This mediator model takes the complex model's inputs and outputs along with domain knowledge to generate interpretable explanations that bridge the gap between complex predictions and human understanding.
Solution Approach 2:
The explanation generation process is segmented into multiple components: obtaining description information from the complex model, acquiring domain knowledge from external sources, and synthesizing these through the second model to produce tailored explanations. This segmentation allows the complex model to maintain its predictive power while the explanation system handles interpretability separately.
2Ease of operation
If existing interpretability techniques are used to explain machine learning models, then some level of explanation is provided, but the explanations cannot be tailored to diverse audiences with varying expertise
Solution Approach 1:
The explanation generation system is made dynamic by allowing the second machine learning model to adapt its output based on audience characteristics. The model can tailor explanations to different levels of expertise, adjusting the complexity, terminology, and detail according to the target audience's background and needs.
Solution Approach 2:
Different parts of the explanation are customized according to the specific audience's requirements. The system can provide more technical details for expert audiences while offering simplified, high-level explanations for non-experts, making each explanation locally optimized for its intended audience.
3Ease of manufacture
If machine learning models operate as black boxes to maintain simplicity of deployment, then deployment ease is improved, but ethical and transparency issues worsen
Solution Approach 1:
The second machine learning model serves as an intermediary that preserves the black-box nature of the first model for deployment purposes while simultaneously generating transparent explanations. This allows the complex model to be deployed without modification while the mediator handles the transparency and ethical accountability requirements.
Solution Approach 2:
Instead of modifying the original complex model, the system creates a separate explanation model that copies and interprets the first model's behavior. This copying approach maintains the original model's integrity and deployment simplicity while providing the necessary transparency through the explanation model.
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.