Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative? (2412.15920v1)

Abstract: As ML systems are increasingly adopted across industries, addressing fairness and bias has become essential. While many solutions focus on ethical challenges in ML, recent studies highlight that data itself is a major source of bias. Pre-processing techniques, which mitigate bias before training, are effective but may impact model performance and pose integration difficulties. In contrast, fairness-aware Data Preparation practices are both familiar to practitioners and easier to implement, providing a more accessible approach to reducing bias. Objective. This registered report proposes an empirical evaluation of how optimally selected fairness-aware practices, applied in early ML lifecycle stages, can enhance both fairness and performance, potentially outperforming standard pre-processing bias mitigation methods. Method. To this end, we will introduce FATE, an optimization technique for selecting 'Data Preparation' pipelines that optimize fairness and performance. Using FATE, we will analyze the fairness-performance trade-off, comparing pipelines selected by FATE with results by pre-processing bias mitigation techniques.

• The paper presents a novel fairness-aware data preparation method using FATE, a genetic algorithm balancing fairness and performance.
• FATE employs a fitness function that integrates PR-AUC and fairness metrics like statistical parity and equal opportunity difference.
• Empirical results demonstrate that FATE-selected pipelines effectively compare with traditional bias mitigation methods while reducing complexity.

Data Preparation for Fairness-Performance Trade-Offs in Machine Learning

The paper "Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative?" introduces a novel approach to mitigate bias in ML systems during the early stages of the ML lifecycle. The authors recognize the critical issue of unfair biases that can arise from training data, which leads to ethical and legal challenges in various application fields. Traditional methods to tackle these biases largely fall into three categories: pre-processing, in-processing, and post-processing, with each having its efficiency and integration challenges.

The study proposes a viable alternative through fairness-aware data preparation. This approach is presented as being more practitioner-friendly due to its alignment with common data preparation practices, which are well-integrated into ML workflows. The key focus is on employing early-stage interventions during the data preparation phase to address fairness before model training.

FATE: An Optimization Technique

Central to the paper is the introduction of FATE (Fairness-Aware Trade-Off Enhancement), a genetic algorithm-based optimization technique. FATE aims to select data preparation pipelines optimizing both fairness and performance. The fairness-aware practices emphasized in the paper include standard scaling, MinMax scaling, resampling, clustering, inverse probability weighting, and matching.

FATE operates by evaluating pipelines through a fitness function that balances predictive performance and fairness. Performance is assessed using PR-AUC, while fairness is gauged by metrics such as statistical parity difference, equal opportunity difference, and disparate impact. FATE's adaptability to various datasets and contexts is highlighted as a major advantage, allowing it to generalize fairness practices effectively.

Empirical Evaluation and Research Questions

The research undertakes a comprehensive empirical evaluation with datasets that incorporate sensitive attributes, aiming to answer two key questions regarding the efficacy and comparative performance of the FATE approach:

1. The efficacy of FATE in selecting near-optimal fairness-aware data preparation configurations.
2. The performance comparison of FATE-selected pipelines against existing pre-processing bias mitigation methods like FairSMOTE, Reweighing, and Disparate Impact Remover.

The evaluation involves various parameter settings for FATE to identify optimal configurations that maximize fairness while maintaining or enhancing performance. A rigorous comparison of these outcomes with state-of-the-art methods is conducted, using non-parametric statistical tests to establish the significance of differences observed.

Implications and Future Directions

The findings of this paper bear significant implications for both theoretical and practical fields in software engineering. Theoretically, it advances our understanding of deploying generic, fairness-aware methodologies during ML data preparation. Practically, it provides ML practitioners with an easily implementable, robust alternative to conventional bias mitigation strategies, potentially reducing the computational overhead and integration complexity associated with existing methods.

The research opens avenues for further exploration into more refined and context-specific fairness metrics that can be seamlessly integrated into FATE, enhancing its applicability across various domains. Additionally, exploring different machine learning models and tasks extends the generalizability of this approach.

In conclusion, this paper contributes meaningfully to the ongoing discourse around achieving fairness without compromising accuracy in machine learning, presenting a balanced, pragmatic approach that aligns closely with practical needs and workflow constraints faced by practitioners in contemporary data-driven environments.