Work

Bias Mitigation Methods for Binary Classification Decision-Making Systems: Survey and Recommendations

Year: 2023

Type: preprint

Abstract: Bias mitigation methods for binary classification decision-making systems have been widely researched due to the ever-growing importance of designing fair machine learning processes that are impartial and do not discriminate against individuals or groups based on protected personal characteristics. In this paper, we present a structured overview of the research landscape for bias mitigation methods, report on their benefits and limitations, and provide recommendations for the development of future bias mitigation methods for binary classification.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2305.20020

Authors: Waller, Madeleine, Rodrigues, Odinaldo, Cocarascu, Oana

Institution: King's College London

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Reduced inequalities

Open Access status: green

Funder: UK Research and Innovation