Algorithmic Fairness: A Tolerance Perspective
It addresses fairness concerns in algorithmic decision-making for society, but is incremental as a survey with a new taxonomy.
This survey tackles the problem of algorithmic fairness by introducing a novel taxonomy based on 'tolerance' to structure understanding of fairness outcomes, synthesizing insights from diverse industries to outline challenges and propose future directions for equitable systems.
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.