Awareness in practice: tensions in access to sensitive attribute data for antidiscrimination

By Miranda Bogen, A. Rieke, Shazeda Ahmed. 2019

54 citations recorded by the source corpus.

Organizations cannot address demographic disparities that they cannot see. Recent research on machine learning and fairness has emphasized that awareness of sensitive attributes, such as race and sex, is critical to the development of interventions. However, on the ground, the existence of these data cannot be taken for granted. This paper uses the domains of employment, credit, and healthcare in the United States to surface conditions that have shaped the availability of sensitive attribute data. For each domain, we describe how and when private companies collect or infer sensitive attribute data for antidiscrimination purposes. An inconsistent story emerges: Some companies are required by law to collect sensitive attribute data, while others are prohibited from doing so. Still others, in the absence of legal mandates, have determined that collection and imputation of these data are appropriate to address disparities. This story has important implications for fairness research and its future applications. If companies that mediate access to life opportunities are unable or hesitant to collect or infer sensitive attribute data, then proposed techniques to detect and mitigate bias in machine learning models might never be implemented outside the lab. We conclude that today's legal requirements and corporate practices, while highly inconsistent across domains, offer lessons for how to approach the collection and inference of sensitive data in appropriate circumstances. We urge stakeholders, including machine learning practitioners, to actively help chart a path forward that takes both policy goals and technical needs into account.

Subfields: AI Ethics, Fairness & Society, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In AI Ethics, Fairness & Society

1. Hanna M. Wallach
2. Solon Barocas
3. Jennifer Wortman Vaughan
4. Adrian Weller
5. Berk Ustun

In Social Sciences & Humanities

1. Hanna M. Wallach
2. Meredith Ringel Morris
3. Solon Barocas
4. Jennifer Wortman Vaughan
5. Adrian Weller

Citations in the corpus

• Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs (2021, background): “6, inferring group membership can be problematic due to the possibility of inaccuracies and due to questions about the ethics of imposing labels on people [2, 12].”
• What We Can't Measure, We Can't Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness (2020, neutral): “[14]”