  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.
