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

TL;DR

This paper explores the complex challenges and ethical dilemmas faced by practitioners in collecting and using demographic data for fairness in algorithms, highlighting normative questions and practical barriers.

Contribution

It provides empirical insights from interviews into the real-world difficulties and ethical considerations in demographic data procurement for algorithmic fairness.

Findings

Practitioners face legal, ethical, and practical barriers to collecting demographic data.

There is a tension between privacy concerns and the need for data to detect bias.

Normative questions about when and how to collect demographic data are central to fairness efforts.

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.