What is Fair? Exploring Pareto-Efficiency for Fairness Constrained Classifiers

10/30/2019
by   Ananth Balashankar, et al.
15

The potential for learned models to amplify existing societal biases has been broadly recognized. Fairness-aware classifier constraints, which apply equality metrics of performance across subgroups defined on sensitive attributes such as race and gender, seek to rectify inequity but can yield non-uniform degradation in performance for skewed datasets. In certain domains, imbalanced degradation of performance can yield another form of unintentional bias. In the spirit of constructing fairness-aware algorithms as societal imperative, we explore an alternative: Pareto-Efficient Fairness (PEF). Theoretically, we prove that PEF identifies the operating point on the Pareto curve of subgroup performances closest to the fairness hyperplane, maximizing multiple subgroup accuracy. Empirically we demonstrate that PEF outperforms by achieving Pareto levels in accuracy for all subgroups compared to strict fairness constraints in several UCI datasets.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/04/2021

Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing

As algorithmic decision-making systems are becoming more pervasive, it i...
research
05/31/2021

Rawlsian Fair Adaptation of Deep Learning Classifiers

Group-fairness in classification aims for equality of a predictive utili...
research
10/25/2019

Toward a better trade-off between performance and fairness with kernel-based distribution matching

As recent literature has demonstrated how classifiers often carry uninte...
research
05/31/2022

To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier

Algorithmic fairness has emerged as an important consideration when usin...
research
06/12/2023

Unprocessing Seven Years of Algorithmic Fairness

Seven years ago, researchers proposed a postprocessing method to equaliz...
research
03/09/2022

Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers

Algorithmic fairness is frequently motivated in terms of a trade-off in ...
research
02/05/2023

The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default

In recent years fairness in machine learning (ML) has emerged as a highl...

Please sign up or login with your details

Forgot password? Click here to reset