Loss-Aversively Fair Classification

Abstract

The use of algorithmic (learning-based) decision making in scenarios that affect human lives has motivated a number of recent studies to investigate such decision making systems for potential unfairness, such as discrimination against subjects based on their sensitive features like gender or race. However, when judging the fairness of a newly designed decision making system, these studies have overlooked an important influence on people's perceptions of fairness, which is how the new algorithm changes the status quo, i.e., decisions of the existing decision making system. Motivated by extensive literature in behavioral economics and behavioral psychology (prospect theory), we propose a notion of fair updates that we refer to as loss-averse updates. Loss-averse updates constrain the updates to yield improved (more beneficial) outcomes to subjects compared to the status quo. We propose tractable proxy measures that would allow this notion to be incorporated in the training of a variety of linear and non-linear classifiers. We show how our proxy measures can be combined with existing measures for training nondiscriminatory classifiers. Our evaluation using synthetic and real-world datasets demonstrates that the proposed proxy measures are effective for their desired tasks.

arXiv comment8 pages, Accepted at AIES 2019

References in corpus (10)

• Equality of Opportunity in Supervised Learning
• Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
• All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
• On the Complexity of Best Arm Identification in Multi-Armed Bandit Models
• Fairness of Exposure in Rankings
• CVXPY: A Python-Embedded Modeling Language for Convex Optimization
• A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices
• A Convex Framework for Fair Regression
• From Parity to Preference-based Notions of Fairness in Classification
• Operationalizing Individual Fairness with Pairwise Fair Representations

Cited by in corpus (4)

• Rank List Sensitivity of Recommender Systems to Interaction Perturbations
• Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools
• Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints
• Algorithmic Fairness Datasets: the Story so Far