Machine Learning Explanation Apparatus Using Training Instance Evidence
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Solution Overview
Problem
Users lack understanding of machine learning model decisions, leading to mistrust and underutilization of automated systems, as the reasoning behind classification decisions is unknown.
Innovation Solution
A computing system that processes data to identify and present relevant training instances as evidence to explain classification decisions made by machine learning models, using techniques such as k-nearest neighbors graphs and shortest-path trees to communicate this evidence to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to automate decision-making, then system capabilities and efficiency are enhanced, but user trust and understanding of model decisions deteriorate due to unknown reasoning processes
Solution Approach 1:
The patent introduces an explanation generation system as an intermediary between the machine learning model and the user. This mediator translates the model's internal reasoning processes into human-understandable explanations, preserving the benefits of automation while restoring transparency. The explanation system processes model decisions and presents them in formats that maintain user trust without reducing system capabilities.
Solution Approach 2:
The patent replaces the opaque 'black box' mechanical system of traditional ML decision-making with a transparent explanation-generating system. By substituting the unknown reasoning process with an explainable interface that generates human-readable justifications, the system maintains automation efficiency while eliminating the information loss about decision rationale.
2Extent of automation
If machine learning models operate as black boxes, then automation efficiency is maintained, but user trust and appropriate utilization deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where explanations of model decisions are provided to users, allowing them to understand and verify the reasoning process. This feedback loop enhances user trust by making the automation system's behavior predictable and understandable, while maintaining the efficiency of automated operation. Users can provide feedback on explanation quality, further improving the system.
3Loss of information
If explanations of model decisions are provided to users, then user understanding and trust are enhanced, but system complexity increases
Solution Approach 1:
The patent segments the explanation generation function as a separate module from the core machine learning model. This segmentation allows the model to maintain its optimized, simple structure for efficient decision-making, while the explanation module handles the complexity of generating human-understandable rationales. The separation prevents complexity from propagating to the core model while still providing transparent explanations.
Data Source
AI summary
Explanation apparatus and methods are described. In one aspect, an explanation apparatus includes processing circuity configured to access a source instance which has been classified by a machine learning model; create associations of the source instance with a plurality of training instances; and process the associations of the source instance and the training instances to identify a first subset of the training instances which have less relevance to the classification decision of the source instance by the machine learning model compared with a second subset of the training instances; and an interface configured to communicate information to a user, and wherein the processing circuitry is configured to control the user interface to communicate the second subset of the training instances to the user as evidence to explain the classification of the source instance by the machine learning model.