Long grass and folilage

Abstract

How can we build more just machine learning systems? To answer this question, we need to know both what justice is and how to tell whether one system is more or less just than another. That is, we need both a definition and a measure of justice. Theories of distributive justice hold that justice can be measured (in part) in terms of the fair distribution of benefits and burdens across people in society. Recently, the field known as fair machine learning has turned to John Rawls’s theory of distributive justice for inspiration and operationalization. However, philosophers known as capability theorists have long argued that Rawls’s theory uses the wrong measure of justice, thereby encoding biases against people with disabilities. If these theorists are right, is it possible to operationalize Rawls’s theory in machine learning systems without also encoding its biases? In this paper, I draw on examples from fair machine learning to suggest that the answer to this question is no: the capability theorists’ arguments against Rawls’s theory carry over into machine learning systems. But capability theorists don’t only argue that Rawls’s theory uses the wrong measure, they also offer an alternative measure. Which measure of justice is right? And has fair machine learning been using the wrong one?

Related Media

Full Paper (pdf)
Full Paper (arXiv)
FAccT Conference Presentation (Slides & Transcript)

Committee

This paper was submitted in partial fulfillment of the requirements for the degree of Master’s of Electrical Engineering and Computer Science at MIT (September 2020).

Arvind Satyanarayan (EECS)
Sally Haslanger (Philosophy)
Reuben Binns (Human-Centered Computing)

Citation

This paper was presented at the ACM Conference on Fairness, Accountability, and Transparency (30 January 2020) and at the ACM SIGACCESS Conference on Computers and Accessibility: Workshop on AI Fairness for People with Disabilities (27 October 2019).

Measuring Justice in Machine Learning
Alan Lundgard
ACM Conference on Fairness, Accountability, Transparency (FAccT), 2020.

Bibtex

@inproceedings{2020-measuring-justice-machine-learning,
 title = {Measuring Justice in Machine Learning},
 author = {Alan Lundgard},
 booktitle = {ACM Conference on Fairness, Accountability, Transparency (FAccT)},
 year = {2020},
 url = {https://dl.acm.org/doi/abs/10.1145/3351095.3372838}
}