The Disparate Benefits of Deep Ensembles

Abstract: Ensembles of Deep Neural Networks, Deep Ensembles, are widely used as a simple way to boost predictive performance. However, their impact on algorithmic fairness is not well understood yet. Algorithmic fairness examines how a model's performance varies across socially relevant groups defined by protected attributes such as age, gender, or race. In this work, we explore the interplay between the performance gains from Deep Ensembles and fairness. Our analysis reveals that they unevenly favor different groups, a phenomenon that we term the disparate benefits effect. We empirically investigate this effect using popular facial analysis and medical imaging datasets with protected group attributes and find that it affects multiple established group fairness metrics, including statistical parity and equal opportunity. Furthermore, we identify that the per-group differences in predictive diversity of ensemble members can explain this effect. Finally, we demonstrate that the classical Hardt post-processing method is particularly effective at mitigating the disparate benefits effect of Deep Ensembles by leveraging their better-calibrated predictive distributions.

• The paper reveals that Deep Ensembles can improve predictive performance while disproportionately benefiting already advantaged groups.
• It identifies varying predictive diversity among ensemble members as a key factor driving fairness disparities across datasets.
• The study proposes group-specific threshold adjustments via Hardt post-processing to mitigate fairness violations without sacrificing accuracy.

Overview of "The Disparate Benefits of Deep Ensembles"

The paper "The Disparate Benefits of Deep Ensembles" presents an empirical study exploring the impacts of Deep Ensembles on algorithmic fairness. Deep Ensembles are popular for enhancing predictive performance and uncertainty estimation in deep learning models. However, their effects on fairness across groups identified by protected attributes have not been extensively explored, a gap this paper seeks to address.

Core Contributions

1. Disparate Benefits Effect: The paper introduces the "disparate benefits effect," revealing that Deep Ensembles, while generally improving overall performance, can disproportionately benefit already advantaged groups. The effect is particularly highlighted across diverse datasets, including facial analysis and medical imaging.
2. Analysis of Predictive Diversity: The research identifies differences in predictive diversity among individual ensemble members across groups as a primary cause of the disparate benefits effect. It suggests that variations in base model diversity can lead to uneven performance enhancements when aggregated.
3. Mitigation Strategies: The authors propose the use of post-processing techniques, specifically Hardt post-processing, to address fairness violations without sacrificing performance gains. Deep Ensembles' improved calibration over individual models enhances their susceptibility to prediction threshold adjustment, allowing for effective post-processing.

Methodology

The study evaluated various Deep Ensemble configurations on datasets from facial recognition (FairFace and UTKFace) and medical imaging (CheXpert), utilizing several group fairness metrics like Statistical Parity Difference (SPD), Equal Opportunity Difference (EOD), and Average Odds Difference (AOD). The analysis covered fifteen tasks across different architectures and highlighted when and why disparate benefits occur.

Findings and Implications

• Empirical Evidence:

Strong results demonstrate that performance increases do not equate to equitable treatment across groups. In many instances, the addition of ensemble members enhanced performance but at the cost of increased fairness violations, particularly in scenarios with existing high fairness disparities.

• Predictive Diversity Analysis:

The paper posits that discrepancies in predictive diversity among ensemble members are a crucial factor for disparate benefits. Experiments revealed that groups with higher average predictive diversity among ensemble members tend to receive more performance benefits from ensembling.

• Mitigation Approach:

The proposed mitigation approach leverages post-processing by adapting group-specific decision thresholds. This adjustment ensures fairness constraints are better adhered to, thus maintaining performance improvements while aligning with fairness goals.

Future Directions

The study's exploration prompts several avenues for further research:

• Broadening Scope:

Extending the investigation beyond vision datasets to include other domains such as natural language processing could provide broader insights into the effects of Deep Ensembles on fairness.

• Comprehensive Fairness Metrics:

Future work could explore additional fairness metrics, including individual fairness and causal fairness frameworks, to provide a more holistic understanding of fairness in AI models.

• Fairness During Training:

Another potential area is integrating fairness considerations into the training process of individual ensemble members, which could complement post-processing strategies.

The paper clearly contributes to both the understanding and the mitigation of fairness issues caused by Deep Ensembles, providing a pathway for more equitable machine learning applications. By situating these findings within a broader context of algorithmic fairness in high-stakes domains, the study offers a critical perspective on advancing both performance and fairness in AI systems.