Algorithmic Fairness: A Tolerance Perspective

TL;DR

This paper addresses the problem of algorithmic fairness and its social consequences by introducing a tolerance-based perspective. It presents a taxonomy of fairness grounded in legal, ethical, and personal tolerance, and provides a systematic review across industries. It identifies emerging challenges and proposes directions for research and policy to advance equitable algorithmic systems. The work aims to guide practitioners and policymakers toward fairness-aware designs that respect diverse normative standards.

Abstract

Recent advancements in machine learning and deep learning have brought algorithmic fairness into sharp focus, illuminating concerns over discriminatory decision making that negatively impacts certain individuals or groups. These concerns have manifested in legal, ethical, and societal challenges, including the erosion of trust in intelligent systems. In response, this survey delves into the existing literature on algorithmic fairness, specifically highlighting its multifaceted social consequences. We introduce a novel taxonomy based on 'tolerance', a term we define as the degree to which variations in fairness outcomes are acceptable, providing a structured approach to understanding the subtleties of fairness within algorithmic decisions. Our systematic review covers diverse industries, revealing critical insights into the balance between algorithmic decision making and social equity. By synthesizing these insights, we outline a series of emerging challenges and propose strategic directions for future research and policy making, with the goal of advancing the field towards more equitable algorithmic systems.

Figures (4)

• Figure 1: The tolerance and fairness issues.
• Figure 2: Algorithmic fairness issues exacerbating social discrimination.
• Figure 3: Noncompetitive price.
• Figure 4: Long-tail items.