‘AI Theory of Justice’: Using Rawlsian Approaches to Better Legislate on Machine Learning in Government

24 Pages Posted: 5 Jun 2020

See all articles by Jamie Grace

Jamie Grace

Sheffield Hallam University

Date Written: April 29, 2020

Abstract

Policy-making is increasingly being informed by 'big data' technologies of analytics, machine learning, and artificial intelligence (AI). John Rawls used particular principles of reasoning in his 1971 book A Theory of Justice which might help explore known problems of data bias, unfairness, accountability and privacy, in relation to applications of machine learning and AI in government. This paper will investigate how the current assortment of UK governmental policy and regulatory developments around AI in the public sector could be said to meet, or not meet, these Rawlsian principles, and what we might do better by incorporating them when we respond legislatively to this ongoing challenge. This paper uses a case study of data analytics and machine learning regulation as the central means of this exploration of Rawlsian thinking in relation to the re-development of algorithmic governance. This paper was written with Roxanne Bamford of the Tony Blair Institute for Global Change.

Keywords: Data, Algorithms, Machine Learning, Fairness, Rawls, Justice, Privacy, Bias, Transparency, Accountability, Data Protection, Human Rights

Suggested Citation

Grace, Jamie, ‘AI Theory of Justice’: Using Rawlsian Approaches to Better Legislate on Machine Learning in Government (April 29, 2020). Available at SSRN: https://ssrn.com/abstract=3588256 or http://dx.doi.org/10.2139/ssrn.3588256

Jamie Grace (Contact Author)

Sheffield Hallam University ( email )

United Kingdom

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
475
Abstract Views
1,635
Rank
110,659
PlumX Metrics