(Unfair) Norms in Fairness Research: A Meta-Analysis

TL;DR

This paper conducts a meta-analysis of AI fairness research, revealing US-centric biases and binary identity categorizations, and advocates for more inclusive, globally aware fairness frameworks in AI development.

Contribution

It uncovers prevalent biases and simplistic identity classifications in fairness research and proposes a shift towards more nuanced, inclusive approaches to defining algorithmic fairness.

Findings

US-centric dominance in fairness research

Widespread use of binary identity categories

Lack of representation of diverse global contexts

Abstract

Algorithmic fairness has emerged as a critical concern in artificial intelligence (AI) research. However, the development of fair AI systems is not an objective process. Fairness is an inherently subjective concept, shaped by the values, experiences, and identities of those involved in research and development. To better understand the norms and values embedded in current fairness research, we conduct a meta-analysis of algorithmic fairness papers from two leading conferences on AI fairness and ethics, AIES and FAccT, covering a final sample of 139 papers over the period from 2018 to 2022. Our investigation reveals two concerning trends: first, a US-centric perspective dominates throughout fairness research; and second, fairness studies exhibit a widespread reliance on binary codifications of human identity (e.g., "Black/White", "male/female"). These findings highlight how current research often overlooks the complexities of identity and lived experiences, ultimately failing to represent diverse global contexts when defining algorithmic bias and fairness. We discuss the limitations of these research design choices and offer recommendations for fostering more inclusive and representative approaches to fairness in AI systems, urging a paradigm shift that embraces nuanced, global understandings of human identity and values.