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Cited by 112Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)AI and HR Technologies

The DOI matches its Crossref record; 55 of the 58 references it cites were found in a registry or an index.

What We Can't Measure, We Can't Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness

Andrus, McKane, Spitzer, Elena, Brown, Jeffrey, Xiang, Alice·2021·Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
DOI 10.1145/3442188.3445888
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Abstract

As calls for fair and unbiased algorithmic systems increase, so too does the number of individuals working on algorithmic fairness in industry. However, these practitioners often do not have access to the demographic data they feel they need to detect bias in practice. Even with the growing variety of toolkits and strategies for working towards algorithmic fairness, they almost invariably require access to demographic attributes or proxies. We investigated this dilemma through semi-structured interviews with 38 practitioners and professionals either working in or adjacent to algorithmic fairness. Participants painted a complex picture of what demographic data availability and use look like on the ground, ranging from not having access to personal data of any kind to being legally required to collect and use demographic data for discrimination assessments. In many domains, demographic data collection raises a host of difficult questions, including how to balance privacy and fairness, how to define relevant social categories, how to ensure meaningful consent, and whether it is appropriate for private companies to infer someone's demographics. Our research suggests challenges that must be considered by businesses, regulators, researchers, and community groups in order to enable practitioners to address algorithmic bias in practice. Critically, we do not propose that the overall goal of future work should be to simply lower the barriers to collecting demographic data. Rather, our study surfaces a swath of normative questions about how, when, and whether this data should be procured, and, in cases where it is not, what should still be done to mitigate bias.

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Metadata

Authors
Andrus, M., Spitzer, E., Brown, J., & Xiang, A.
Affiliations
  • Partnership on AI
  • Minnesota State University, Mankato (US)
  • Sony AI, Partnership on AI
ORCID
Venue
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
Publisher
ACM
Published
2021-03
Pages
249-260
DOI
10.1145/3442188.3445888
OpenAlex
W3135773605
Cited by
111
References
58
Citations by year
2026: 13 · 2025: 19 · 2024: 18 · 2023: 30 · 2022: 23 · 2021: 8
FWCI
17.86
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
Keywords
algorithmic fairness, algorithmic bias, demographic data, data availability, privacy, consent, social categories, bias mitigation, practitioner perspectives, data collection ethics

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