The Sharpe predictor for fairness in machine learning

08/13/2021
by   Suyun Liu, et al.
0

In machine learning (ML) applications, unfair predictions may discriminate against a minority group. Most existing approaches for fair machine learning (FML) treat fairness as a constraint or a penalization term in the optimization of a ML model, which does not lead to the discovery of the complete landscape of the trade-offs among learning accuracy and fairness metrics, and does not integrate fairness in a meaningful way. Recently, we have introduced a new paradigm for FML based on Stochastic Multi-Objective Optimization (SMOO), where accuracy and fairness metrics stand as conflicting objectives to be optimized simultaneously. The entire trade-offs range is defined as the Pareto front of the SMOO problem, which can then be efficiently computed using stochastic-gradient type algorithms. SMOO also allows defining and computing new meaningful predictors for FML, a novel one being the Sharpe predictor that we introduce and explore in this paper, and which gives the highest ratio of accuracy-to-unfairness. Inspired from SMOO in finance, the Sharpe predictor for FML provides the highest prediction return (accuracy) per unit of prediction risk (unfairness).

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/03/2020

Accuracy and Fairness Trade-offs in Machine Learning: A Stochastic Multi-Objective Approach

In the application of machine learning to real-life decision-making syst...
research
05/22/2023

Causality-Aided Trade-off Analysis for Machine Learning Fairness

There has been an increasing interest in enhancing the fairness of machi...
research
07/14/2021

On the impossibility of non-trivial accuracy under fairness constraints

One of the main concerns about fairness in machine learning (ML) is that...
research
02/17/2023

Learning with Impartiality to Walk on the Pareto Frontier of Fairness, Privacy, and Utility

Deploying machine learning (ML) models often requires both fairness and ...
research
09/15/2022

Efficient first-order predictor-corrector multiple objective optimization for fair misinformation detection

Multiple-objective optimization (MOO) aims to simultaneously optimize mu...
research
06/17/2021

On Anytime Learning at Macroscale

Classical machine learning frameworks assume access to a possibly large ...
research
10/02/2021

Consider the Alternatives: Navigating Fairness-Accuracy Tradeoffs via Disqualification

In many machine learning settings there is an inherent tension between f...

Please sign up or login with your details

Forgot password? Click here to reset