Fairness Auditing System for ML Model Bias Detection
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Solution Overview
Problem
Machine learning algorithms, particularly 'black box' models, are challenging to audit for fairness due to their opaque nature, making it difficult to detect biases and ensure fairness in decision-making processes, especially in real-world applications where visualizing and measuring fairness is statistically complex.
Innovation Solution
A computer-implemented method and system for measuring fairness in machine learning models by obtaining a deployed model and an audit dataset, specifying fairness criteria, evaluating the model's performance against these criteria, and generating a visual diagnostic diagram to analyze potential failures, allowing for iterative evaluation and user input-driven output revisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning algorithms are used to make predictions and identify patterns, then productivity and decision-making capability are improved, but the algorithms become black box systems that are not susceptible to conventional auditing techniques
Solution Approach 1:
The patent introduces an intermediary auditing system that acts as a mediator between the black box machine learning algorithm and human reviewers. This system captures inputs, outputs, and intermediate states, transforming the opaque algorithmic process into an auditable format without modifying the original algorithm's predictive capabilities.
Solution Approach 2:
The patent creates a copy or representation of the algorithm's decision-making process through structured data capture. By recording inputs, outputs, and intermediate states in a standardized format, the system enables auditing of algorithmic behavior without requiring access to or modification of the original black box algorithm.
2Loss of information
If conventional auditing techniques are applied to black box algorithms, then transparency is improved, but the inability to observe internal processes limits the effectiveness of such auditing
Solution Approach 1:
The patent performs preliminary action by capturing and storing algorithmic inputs, outputs, and intermediate states before the actual decision-making process completes. This proactive data capture ensures that all necessary information is available for subsequent fairness auditing, eliminating the need to probe or reverse-engineer the black box algorithm.
Solution Approach 2:
The patent implements feedback mechanisms that allow auditors to review captured algorithmic behavior and provide input for improving fairness metrics. The system continuously monitors and records algorithmic decisions, enabling iterative refinement of fairness assessments based on observed patterns and outcomes.
3Adaptability or versatility
If fairness metrics are evaluated across multiple population groups, then fairness measurement comprehensiveness is improved, but the complexity of specifying and evaluating criteria across groups increases
Solution Approach 1:
The patent creates a universal auditing framework that can evaluate fairness across multiple population groups using a standardized set of metrics and criteria. The system is designed to handle diverse demographic groups and fairness definitions through a single multi-functional platform, eliminating the need for separate auditing systems for different groups.
Solution Approach 2:
The patent segments the fairness evaluation process into distinct components: population group identification, metric selection, criterion specification, and result aggregation. This segmentation allows auditors to systematically evaluate each group against relevant criteria while maintaining overall coherence through standardized data structures and evaluation protocols.
Data Source
AI summary
An example computer-implemented method for measuring fairness includes obtaining a deployed model and an audit dataset associated with the deployed model, where the audit dataset is configured to evaluate model fidelity against one or more fairness metrics; specifying a fairness criterion on a plurality of population groups, the fairness criterion including one or more fairness metrics; performing an evaluation of the deployed model with respect to the fairness criterion, where the evaluation of the fairness criterion includes analyzing the audit dataset using the deployed model to predict a respective outcome metric for each of the population groups; and generating a visual diagnostic diagram for facilitating an analysis of potential failures of the deployed model with respect to the specified fairness criterion.