Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML (2306.09297v3)

Abstract: Machine learning (ML) is increasingly being used in critical decision-making software, but incidents have raised questions about the fairness of ML predictions. To address this issue, new tools and methods are needed to mitigate bias in ML-based software. Previous studies have proposed bias mitigation algorithms that only work in specific situations and often result in a loss of accuracy. Our proposed solution is a novel approach that utilizes automated machine learning (AutoML) techniques to mitigate bias. Our approach includes two key innovations: a novel optimization function and a fairness-aware search space. By improving the default optimization function of AutoML and incorporating fairness objectives, we are able to mitigate bias with little to no loss of accuracy. Additionally, we propose a fairness-aware search space pruning method for AutoML to reduce computational cost and repair time. Our approach, built on the state-of-the-art Auto-Sklearn tool, is designed to reduce bias in real-world scenarios. In order to demonstrate the effectiveness of our approach, we evaluated our approach on four fairness problems and 16 different ML models, and our results show a significant improvement over the baseline and existing bias mitigation techniques. Our approach, Fair-AutoML, successfully repaired 60 out of 64 buggy cases, while existing bias mitigation techniques only repaired up to 44 out of 64 cases.

Analyzing Fair-AutoML: Balancing Fairness and Accuracy in Machine Learning Models

The paper "Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML" presents a comprehensive approach to addressing bias in ML models by leveraging automated machine learning (AutoML) techniques. In recent years, the use of ML in decision-making systems has come under scrutiny due to incidents of bias and discrimination, raising the necessity for effective bias mitigation methods that do not compromise predictive accuracy. This research demonstrates how Fair-AutoML solves this dilemma efficiently.

Main Contributions

The authors propose two key innovations within Fair-AutoML: a dynamic optimization function to balance fairness and accuracy, and a search space pruning technique to enhance computational efficiency in repairing unfair models. Built upon the robust capabilities of Auto-Sklearn, the proposed solution integrates seamlessly into existing AutoML frameworks.

1. Dynamic Optimization Function: This feature dynamically generates an objective function that appropriately weighs fairness and accuracy, guided by extracted parameters from the datasets and model output. The core idea involves setting an optimal weight for fairness, defined by the value of $\beta$, to guide AutoML processes towards desired fairness outcomes without degrading the model's accuracy.
2. Search Space Pruning: By pruning the search space based on data characteristics, Fair-AutoML accelerates the bug-fixing process inherent in Bayesian optimization methods used by AutoML systems. This method involves an offline phase to construct a database of configurations and an online phase to match new inputs to existing configurations, reducing computational overhead.

Evaluation and Results

The effectiveness of Fair-AutoML is rigorously evaluated against existing bias mitigation strategies and traditional AutoML configurations. The benchmark consists of 16 models sourced from Kaggle, tested across four datasets—Adult Census, Bank Marketing, German Credit, and Titanic— with fairness measured by four criteria: Disparate Impact (DI), Statistical Parity Difference (SPD), Equal Opportunity Difference (EOD), and Average Absolute Odds Difference (AOD).

The results validate Fair-AutoML's superiority, successfully repairing 60 out of 64 fairness issues, compared to a mere 44 achieved by the leading bias mitigation techniques. Notably, Fair-AutoML outperformed Auto-Sklearn, indicative of the targeted efficiency brought about by the dynamic optimization function and the search space pruning techniques.

Implications and Future Directions

Fair-AutoML offers valuable implications for the broader field of machine learning, especially in practical applications where fairness is as critical as predictive performance. This research sheds light on the potential of integrating fairness objectives directly into AutoML frameworks, transforming how fairness considerations can be pragmatically approached in real-world ML systems.

As ML models become more integral across various sectors, the approaches adopted in Fair-AutoML can serve as a foundation for extending fairness and accuracy considerations to more complex and data-intensive domains. Future advancements may explore deep learning integrations and further expansions into diverse ML models, complementing ensemble methods to mitigate broader dimensions of bias.

In conclusion, Fair-AutoML represents a significant step forward in the endeavor to balance fairness and accuracy in ML systems, addressing a longstanding challenge with precision and adaptability. It underscores a pivotal shift towards embedding ethical considerations within the technical evolution of artificial intelligence.