Algorithmic Decision-Making When Humans Disagree About Ends

New Criminal Law Review, Forthcoming

18 Pages Posted: 2 Dec 2020

See all articles by Kiel Brennan-Marquez

Kiel Brennan-Marquez

University of Connecticut - School of Law

Vincent Chiao

University of Toronto - Faculty of Law

Date Written: October 19, 2020

Abstract

Which interpretive tasks should be delegated to machines? This question has become a focal point of “tech governance” debates; one familiar answer is that machines are capable, in principle, of implementing tasks whose ends are uncontroversial, but machine delegation is inappropriate for tasks that elude human consensus. After all, if even (human) experts cannot agree about the nature of a task, what hope is there for machines?

Here, we turn this position around. In fact, when humans disagree about the nature of a task, that should be prima facie grounds for machine-delegation, not against it. The reason comes back to a fairness concern: affected parties should be able to predict the outcomes of particular cases. Indeterminate decision-making environments—those in which human disagree about ends—are inherently unpredictable in the sense that, for any given case, the distribution of likely outcomes will depend on a specific decision-maker’s view of the relevant end. This injects an irreducible—and, we argue, intolerable—dynamic of randomization into the decision-making process from the perspective of non-repeat players. To the extent machine decisions aggregate across disparate views of a task’s relevant ends, they promise improvement, as such, on this specific dimension of predictability; whatever the other virtues and drawbacks of machine decision-making, this gain should be recognized and factored into governance.

The essay has two halves. In the first, we elaborate the formal point, drawing a distinction between determinacy and certainty as epistemic properties and fashioning a taxonomy of decision-types. In the second half, we bring the formal point alive through the case study of criminal sentencing.

Keywords: Algorithms, epistemology, sentencing, judgment

JEL Classification: K1, K10, K14

Suggested Citation

Brennan-Marquez, Kiel and Chiao, Vincent, Algorithmic Decision-Making When Humans Disagree About Ends (October 19, 2020). New Criminal Law Review, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3714407

Kiel Brennan-Marquez (Contact Author)

University of Connecticut - School of Law ( email )

65 Elizabeth Street
Hartford, CT 06105
United States

Vincent Chiao

University of Toronto - Faculty of Law ( email )

78 and 84 Queen's Park
Toronto, Ontario M5S 2C5
Canada

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
33
Abstract Views
140
PlumX Metrics