  Various tools and practices have been developed to support practitioners in
identifying, assessing, and mitigating fairness-related harms caused by AI
systems. However, prior research has highlighted gaps between the intended
design of these tools and practices and their use within particular contexts,
including gaps caused by the role that organizational factors play in shaping
fairness work. In this paper, we investigate these gaps for one such practice:
disaggregated evaluations of AI systems, intended to uncover performance
disparities between demographic groups. By conducting semi-structured
interviews and structured workshops with thirty-three AI practitioners from ten
teams at three technology companies, we identify practitioners' processes,
challenges, and needs for support when designing disaggregated evaluations. We
find that practitioners face challenges when choosing performance metrics,
identifying the most relevant direct stakeholders and demographic groups on
which to focus, and collecting datasets with which to conduct disaggregated
evaluations. More generally, we identify impacts on fairness work stemming from
a lack of engagement with direct stakeholders or domain experts, business
imperatives that prioritize customers over marginalized groups, and the drive
to deploy AI systems at scale.
