ML Model Interpretability via Rule Group Conversion
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Solution Overview
Problem
There is a demand for a technique to easily construct learning models that satisfy necessary standards, particularly in applications involving machine learning, where ensuring compliance with laws and regulations is crucial but often challenging due to the complexity of interpreting model outputs.
Innovation Solution
An information processing apparatus comprising a first learning unit, a second learning unit, an evaluation unit, and an adjustment unit, which enables the construction of a learning model by converting its output into a rule group interpretable by users, evaluating these rules against predetermined standards, and adjusting the learning process to ensure compliance with those standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a learning model is constructed using machine learning techniques, then prediction accuracy and automation capability are improved, but interpretability and ease of compliance verification deteriorate
Solution Approach 1:
The patent introduces an intermediary component that translates the complex internal representations and decision boundaries of machine learning models into human-interpretable formats such as rule sets, natural language explanations, or visualizations. This mediator layer preserves the automated prediction capability while making the decision logic accessible and verifiable against compliance standards.
Solution Approach 2:
The patent segments the learning model into distinct functional components, separating the automated prediction engine from the interpretation and compliance verification modules. This allows the model to maintain its automated decision-making capability while enabling independent verification of individual decision factors against regulatory standards.
2Measurement precision
If complex machine learning algorithms are used to improve prediction accuracy, then model performance is improved, but complexity of compliance verification increases
Solution Approach 1:
The intermediary translation layer converts complex algorithmic decisions into standardized, interpretable representations that can be systematically evaluated against compliance criteria, reducing the complexity of verification despite maintaining high prediction accuracy through sophisticated models.
Solution Approach 2:
The patent implements feedback mechanisms where compliance verification results are fed back into the model construction process, enabling iterative refinement of both the prediction accuracy and compliance interpretability. This feedback loop allows complex models to be adjusted to meet verification requirements without sacrificing performance.
3Reliability
If learning models are adjusted to satisfy regulatory standards, then compliance is improved, but model construction time and complexity increase
Solution Approach 1:
The patent incorporates compliance requirements and interpretation capabilities into the preliminary model design phase, rather than adding them as post-hoc modifications. By pre-integrating interpretable representation mechanisms and compliance checking into the model construction process, the system achieves regulatory satisfaction without requiring complex iterative adjustments.
Data Source
AI summary
An information processing apparatus according to an embodiment of the present technology includes a first learning unit, a second learning unit, an evaluation unit, and an adjustment unit. The first learning unit causes a predetermined learning model to perform learning. The second learning unit causes a conversion model to perform learning, the conversion model converting an output of the predetermined learning model into a rule group described in a format that can be interpreted by a user. The evaluation unit acquires evaluation information obtained by evaluating the rule group in accordance with a predetermined standard. The adjustment unit adjusts learning processing of the predetermined learning model on the basis of the evaluation information.