Paired-Consistency Fairness in Machine Learning Models
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Solution Overview
Problem
Existing machine learning models face challenges in ensuring fairness, particularly in avoiding discrimination based on protected variables like gender and race, even when these variables are not explicitly included in the input data, due to correlated features that can lead to biased predictions.
Innovation Solution
The implementation of a paired-consistency-based process that utilizes domain experts to generate consistency pairs of data points, which are then used to evaluate and regulate the fairness of machine learning models by ensuring similar treatment of instances differing only in protected variables, with the paired-consistency metric integrated into the model's loss function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If protected variables are explicitly removed from input data to avoid discrimination, then fairness is improved, but bias by proxy features still causes discriminatory predictions
Solution Approach 1:
The patent introduces paired-consistency pairs as an intermediary mechanism to detect and regulate bias by proxy. These pairs consist of data points that are identical except for protected attributes, serving as a mediator to expose discriminatory patterns that traditional fairness methods miss. The consistency metric acts as a mediator between model predictions and fairness requirements, enabling regulation of proxy bias without removing protected variables.
2Ease of operation
If domain experts manually evaluate fairness without understanding model internals, then ease of operation is improved, but measurement precision of fairness deteriorates
Solution Approach 1:
The patent replaces subjective expert judgment with an automated paired-consistency metric that objectively measures fairness. Instead of relying on experts' intuitive assessments, the system uses a computational metric that quantifies consistency between predictions on paired data points, substituting mechanical computation for human judgment while maintaining ease of operation.
Solution Approach 2:
The patent implements feedback by integrating the paired-consistency metric into the model training process. The metric provides continuous feedback during training, allowing the model to adjust its predictions to improve fairness. This feedback loop enables precise measurement and regulation of fairness while remaining accessible to domain experts who can interpret and guide the process without needing to understand model internals.
3Reliability
If paired-consistency metric is integrated into loss function to regulate fairness, then fairness is improved, but device complexity increases
Solution Approach 1:
The patent segments the fairness regulation process into distinct components: paired data generation, consistency metric calculation, and loss function integration. This segmentation allows each component to be developed and optimized independently, reducing overall complexity. The paired-consistency metric is calculated separately and then integrated into the existing loss function framework, avoiding complete system redesign.
Solution Approach 2:
The patent creates a universal paired-consistency framework that can be applied to multiple types of machine learning models and fairness requirements. The metric is designed to work with classification, regression, and ranking tasks, and can regulate different fairness constraints (demographic parity, equalized odds, etc.). This multi-functionality reduces complexity by providing a single reusable solution rather than model-specific fairness mechanisms.
Data Source
AI summary
Systems and methods that implement a paired-consistency-based process for evaluating and or regulating fairness in machine learning models.