A comparative study of fairness-enhancing interventions in machine learning

Abstract: Computers are increasingly used to make decisions that have significant impact in people's lives. Often, these predictions can affect different population subgroups disproportionately. As a result, the issue of fairness has received much recent interest, and a number of fairness-enhanced classifiers and predictors have appeared in the literature. This paper seeks to study the following questions: how do these different techniques fundamentally compare to one another, and what accounts for the differences? Specifically, we seek to bring attention to many under-appreciated aspects of such fairness-enhancing interventions. Concretely, we present the results of an open benchmark we have developed that lets us compare a number of different algorithms under a variety of fairness measures, and a large number of existing datasets. We find that although different algorithms tend to prefer specific formulations of fairness preservations, many of these measures strongly correlate with one another. In addition, we find that fairness-preserving algorithms tend to be sensitive to fluctuations in dataset composition (simulated in our benchmark by varying training-test splits), indicating that fairness interventions might be more brittle than previously thought.

• The paper examines how different fairness-enhancing algorithms perform by systematically comparing multiple fairness metrics across diverse datasets.
• The study reveals strong correlations among various fairness measures, indicating potential redundancy and emphasizing the need for unique, insightful metrics.
• The research highlights the sensitivity of fairness-preserving methods to dataset composition changes, underscoring the importance of training stability and preprocessing transparency.

A Comparative Study of Fairness-Enhancing Interventions in Machine Learning

The proliferation of ML systems in decision-making roles that significantly affect individuals' lives has elevated concerns regarding fairness and equity. The paper by Friedler et al. provides a detailed comparative analysis of various fairness-enhancing interventions within ML classifiers and predictors. This research is pivotal, as it systematizes the understanding of how different fairness methodologies perform relative to each other across numerous datasets and measures.

Overview of the Study

The paper presents an open benchmark platform to facilitate the comparative analysis of fairness-enhanced algorithms. This benchmark evaluates algorithms based on several fairness metrics and complements this with a wide array of datasets. The central theme of the research revolves around two questions: the comparative analysis of different techniques and the underlying reasons for observed discrepancies in performance.

Key Findings

The study reveals that while different algorithms tend to favor specific fairness concepts, many fairness measures exhibit strong correlations. This finding implies a redundancy in proliferating fairness measures; attention should be focused on measures that provide unique insights.

A significant observation from the research is that fairness-preserving algorithms are notably sensitive to dataset composition changes. This sensitivity is evidenced in the benchmark through varying training-test splits, which indicates potential brittleness in these interventions.

Recommendations for Future Research

The paper concludes with several pertinent recommendations aimed at enhancing the practice of fairness in ML research:

• Preprocessing Transparency: It is crucial for researchers to document multiple preprocessing pathways when preparing datasets, offering performance metrics across these variations. This transparency aids in ensuring that algorithm comparisons are equitable.
• Conservative Introduction of New Fairness Measures: The introduction of new fairness metrics should be reserved for cases where they provide fundamentally different insights compared to existing measures. A robust combination of class-sensitive error rates with Disparate Impact (DI) or Calibrated Equality of Odds (CV) is proposed as an efficient minimal set.
• Emphasis on Training Stability: Assessing an algorithm's performance across a single training-test split is insufficient for stability evaluation. The authors advocate for multiple randomized splits to better capture an algorithm’s robustness.

Implications and Future Directions

The implications of this study are multifold. Practically, it offers a more standardized framework for evaluating fairness, guiding practitioners towards more reliable and interpretable outcomes. Theoretically, it stresses the need for further exploration into the stability and resilience of fairness interventions under varying conditions.

Future research could explore understanding why certain fairness-preserving interventions exhibit brittleness and how to augment them for greater robustness. Additionally, examining the causal relationships and trade-offs between different fairness measures would be invaluable.

In summary, Friedler et al.'s research is an important contribution toward systematizing fairness in ML, providing a robust foundation for future studies aimed at both improving fairness and understanding its limitations within diverse algorithmic contexts.