Minimax Pareto Fairness: A Multi Objective Perspective (2011.01821v1)

Abstract: In this work we formulate and formally characterize group fairness as a multi-objective optimization problem, where each sensitive group risk is a separate objective. We propose a fairness criterion where a classifier achieves minimax risk and is Pareto-efficient w.r.t. all groups, avoiding unnecessary harm, and can lead to the best zero-gap model if policy dictates so. We provide a simple optimization algorithm compatible with deep neural networks to satisfy these constraints. Since our method does not require test-time access to sensitive attributes, it can be applied to reduce worst-case classification errors between outcomes in unbalanced classification problems. We test the proposed methodology on real case-studies of predicting income, ICU patient mortality, skin lesions classification, and assessing credit risk, demonstrating how our framework compares favorably to other approaches.

Minimax Pareto Fairness: A Multi-Objective Perspective

The paper under review presents a compelling approach to addressing fairness in machine learning models, particularly in contexts where group fairness is paramount. The authors introduce a novel framework termed "Minimax Pareto Fairness" (MMPF), which seeks to balance the disparities in predictive risks across different sensitive groups. This framework is formulated as a Multi-Objective Optimization Problem (MOOP), where each sensitive group's risk forms a separate objective within the optimization process.

The core of the MMPF approach lies in utilizing Pareto optimality to ensure fairness across sensitive groups without incurring unnecessary harm. By achieving the minimax risk and Pareto efficiency, the proposed methodology can potentially lead to models that exhibit zero disparity in predictive outcomes if required by policy. Notably, the algorithm devised by the authors does not necessitate access to sensitive attributes during the test phase, thus ensuring applications in scenarios with imbalanced classification problems.

The paper's contribution is multi-faceted:

1. Formulation and Analysis:
   • The authors have meticulously defined fairness as a MOOP, delineating Pareto optimality as a necessary criterion for fair classifiers. This approach ensures that an increase in one group's predictive risk results in an equivalent or greater decrease in another, preventing unnecessary harm.
2. Development of MMPF Criterion:
   • By adopting the minimax strategy, the authors advocate for selecting classifiers that minimize the worst-performing group's risk across all efficient models. This design choice underscores the importance of optimizing for the least advantageously positioned group, thereby enhancing collective fairness across sensitive attributes.
3. Algorithmic Implementation:
   • An optimization algorithm compatible with deep neural networks is presented to satisfy the Pareto efficiency constraints. This methodology facilitates the adaption of well-established machine learning techniques to complex fairness constraints without needing test-time sensitive attribute information.
4. Practical Applications and Performance Assessment:
   • Extensive testing is conducted on several real-world datasets, including income prediction, ICU patient mortality, skin lesion classification, and credit risk assessment. These evaluations demonstrate the proposed framework's supremacy in mitigating worst-case disparities relative to other approaches.

Theoretical implications of this research are profound. By positioning fairness as a problem of multi-objective optimization with Pareto optimal solutions, the paper introduces a paradigm where trade-offs are explicit and measured, enabling policymakers to make informed decisions regarding fairness criteria. This analytical framework reveals insights into the inherent biases of classifiers, echoing calls for more nuanced and balanced fairness definitions in AI applications.

Practically, the ability to apply the MMPF framework without needing sensitive attributes during actual model deployment broadens the scope for fair AI systems—particularly in scenarios where collecting such data is infeasible or raises ethical concerns. The demonstrated efficiency of the proposed techniques in reducing risk disparities across diverse sensitive groups further underscores the practical viability of the MMPF approach.

In contemplating future avenues, the expansion of MMPF to encompass dynamic attributes and continuously evolving labels presents a promising direction. Moreover, enhancing computational efficiency in large-scale settings, particularly through scalable parallel algorithms, would bolster the framework's applicability across broader domains.

In conclusion, the paper offers a valuable contribution to the discourse on AI fairness. Its robust foundations, combined with empirical validations, highlight the Minimax Pareto framework as a pivotal step forward in aligning machine learning solutions with ethically sound decision-making protocols.