This Thing Called Fairness

By Deirdre K. Mulligan, Joshua A. Kroll, Nitin Kohli, Richmond Y. Wong. 2019

92 citations recorded by the source corpus.

The explosion in the use of software in important sociotechnical systems has renewed focus on the study of the way technical constructs reflect policies, norms, and human values. This effort requires the engagement of scholars and practitioners from many disciplines. And yet, these disciplines often conceptualize the operative values very differently while referring to them using the same vocabulary. The resulting conflation of ideas confuses discussions about values in technology at disciplinary boundaries. In the service of improving this situation, this paper examines the value of shared vocabularies, analytics, and other tools that facilitate conversations about values in light of these disciplinary specific conceptualizations, the role such tools play in furthering research and practice, outlines different conceptions of "fairness" deployed in discussions about computer systems, and provides an analytic tool for interdisciplinary discussions and collaborations around the concept of fairness. We use a case study of risk assessments in criminal justice applications to both motivate our effort--describing how conflation of different concepts under the banner of "fairness" led to unproductive confusion--and illustrate the value of the fairness analytic by demonstrating how the rigorous analysis it enables can assist in identifying key areas of theoretical, political, and practical misunderstanding or disagreement, and where desired support alignment or collaboration in the absence of consensus.

Subfields: Computer Science Applications, Safety Research, Sociology and Political Science, AI Ethics, Fairness & Society

How researchers see this paper

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

In Computer Science Applications

1. Ben Hutchinson
2. Vinodkumar Prabhakaran
3. Emily L. Denton

In Safety Research

1. Vinodkumar Prabhakaran
2. Ben Hutchinson
3. Maryam Ghasemaghaei
4. Kellie Webster
5. Nima Kordzadeh
6. Emily L. Denton

In Sociology and Political Science

1. Ben Hutchinson
2. Vinodkumar Prabhakaran
3. Maryam Ghasemaghaei
4. Nima Kordzadeh
5. Emily L. Denton

In AI Ethics, Fairness & Society

1. Maryam Ghasemaghaei
2. Ben Hutchinson
3. Nima Kordzadeh
4. Vinodkumar Prabhakaran
5. Emily L. Denton

Citations in the corpus

• Social Biases in NLP Models as Barriers for Persons with Disabilities (2020, background): “, in terms of model inputs and outputs—as is common in much of the technical ML literature on fairness (Mulligan et al., 2019).”
• Algorithmic bias: review, synthesis, and future research directions (2021, background): “…explains the sources, consequences, and mitigation strategies in a range of domains from employment and people analytics (Gal et al., 2017) and healthcare (Shaw et al., 2019) to education (Williams et al., 2018), credit markets (Fuster et al., 2018), and criminal justice (Mulligan et al., 2019).”