Work

What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective

Year: 2023

Type: article

Abstract: We review and reflect on fairness notions proposed in machine learning literature and make an attempt to draw connections to arguments in moral and political philosophy, especially theories of justice. We survey dynamic fairness inquiries and further consider the long-term impact induced by current prediction and decision. We present a flowchart that encompasses implicit assumptions and expected outcomes of different fairness inquiries on the data-generating process, the predicted outcome, and the induced impact, respectively. We demonstrate the importance of matching the mission (what kind of fairness to enforce) and the means (which appropriate fairness spectrum to analyze) to fulfill the intended purpose.

Source: ACM Computing Surveys

DOI: 10.1145/3597199

Authors: Zeyu Tang, Jiji Zhang, Kun Zhang

Institutions: Carnegie Mellon University, Chinese University of Hong Kong

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Peace, justice, and strong institutions, Reduced inequalities

Open Access status: hybrid