Unitless Dissimilarity Metrics for Machine Learning Feature Bias Detection
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Solution Overview
Problem
Existing machine learning model evaluation techniques lack transparency, comprehensiveness, and comparability in detecting and mitigating feature bias, leading to suboptimal performance and potential biases in classification and regression models.
Innovation Solution
A computer-implemented method and apparatus that generate unitless dissimilarity metrics using a coefficient of variation to evaluate bias in machine learning models, providing a comprehensive and universally comparable framework for detecting and optimizing bias across different evaluation features and functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learning model evaluation techniques are used, then model performance can be assessed, but the evaluation lacks transparency, comprehensiveness, and comparability in detecting feature bias
Solution Approach 1:
The evaluation process is segmented into distinct components: generating multiple performance metrics for different feature classes, calculating individual dissimilarity metrics for each feature, and then aggregating them into an overall bias metric. This segmentation enables transparent tracking of bias sources while maintaining comprehensive evaluation coverage.
Solution Approach 2:
The dissimilarity metric framework is designed to be universally applicable across different machine learning models, evaluation features, and performance metrics. The unitless nature of the metric allows consistent comparison of bias across diverse evaluation contexts, achieving both comprehensiveness and comparability.
2Reliability
If multiple performance metrics are generated for different feature classes, then comprehensive bias detection is achieved, but the complexity of evaluation increases
Solution Approach 1:
Multiple performance metrics across different feature classes are merged into a unified dissimilarity metric through standardized calculation. The aggregation process combines individual feature dissimilarity metrics into an overall model bias metric, achieving comprehensive detection while simplifying the final evaluation output.
Solution Approach 2:
The evaluation system transforms various performance metrics into a standardized unitless dissimilarity parameter. This parameter transformation enables consistent comparison and aggregation across different feature classes while maintaining the reliability of bias detection through mathematically rigorous calculations.
3Adaptability or versatility
If unitless dissimilarity metrics are generated for comparison, then universally comparable evaluation is achieved, but the computational processing requirements increase
Solution Approach 1:
The system replaces manual or ad-hoc bias evaluation methods with an automated computational framework that systematically generates performance metrics and calculates dissimilarity metrics. This substitution enables universally comparable evaluation while improving efficiency through algorithmic processing of the evaluation pipeline.
Data Source
AI summary
Various embodiments of the present disclosure disclose machine-learning based evaluation techniques for detecting feature bias. An evaluation framework is provided that utilizes new evaluation data structures for comprehensibly evaluating feature bias in machine learning models. The evaluation framework includes receiving evaluation dataset for a machine learning model that includes one or more different feature classes of an evaluation feature. The evaluation framework includes generating, using an evaluation function, at least two performance metrics for the machine learning model and generating a unitless dissimilarity metric for the evaluation feature based at least in part on the first performance metric, the second performance metric, and an average between the first and second performance metrics. In this way, the unitless dissimilarity metric is based on a variability associated with the evaluation feature.