The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems

Canadian Journal of Philosophy 52 (1):26-43 (2022)
  Copy   BIBTEX

Abstract

This article examines the complaint that arbitrary algorithmic decisions wrong those whom they affect. It makes three contributions. First, it provides an analysis of what arbitrariness means in this context. Second, it argues that arbitrariness is not of moral concern except when special circumstances apply. However, when the same algorithm or different algorithms based on the same data are used in multiple contexts, a person may be arbitrarily excluded from a broad range of opportunities. The third contribution is to explain why this systemic exclusion is of moral concern and to offer a solution to address it.

Similar books and articles

Democratizing Algorithmic Fairness.Pak-Hang Wong - 2020 - Philosophy and Technology 33 (2):225-244.
On algorithmic fairness in medical practice.Thomas Grote & Geoff Keeling - 2022 - Cambridge Quarterly of Healthcare Ethics 31 (1):83-94.
Rawls’s Original Position and Algorithmic Fairness.Ulrik Franke - 2021 - Philosophy and Technology 34 (4):1803-1817.
A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity.Hoda Heidari - 2019 - Proceedings of the Conference on Fairness, Accountability, and Transparency 1.
Non-empirical problems in fair machine learning.Teresa Scantamburlo - 2021 - Ethics and Information Technology 23 (4):703-712.
The Use and Misuse of Counterfactuals in Ethical Machine Learning.Atoosa Kasirzadeh & Andrew Smart - 2021 - In ACM Conference on Fairness, Accountability, and Transparency (FAccT 21).
Proceed with Caution.Annette Zimmermann & Chad Lee-Stronach - 2021 - Canadian Journal of Philosophy (1):6-25.
What's Fair about Individual Fairness?Will Fleisher - 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society.
First- and Second-Level Bias in Automated Decision-making.Ulrik Franke - 2022 - Philosophy and Technology 35 (2):1-20.

Analytics

Added to PP
2021-06-24

Downloads
1,060 (#11,601)

6 months
337 (#5,474)

Historical graph of downloads
How can I increase my downloads?