Enslaving the Algorithm: From a "Right to an Explanation" to a "Right to Better Decisions"?

TL;DR

This paper critically examines the limitations of the legal 'right to an explanation' in AI systems, highlighting the gap between transparency and meaningful decision improvement, and advocates for broader governance approaches.

Contribution

It analyzes recent legal developments and introduces alternative governance methods beyond rights-based explanations to improve algorithmic decision-making accountability.

Findings

Legal explanation rights are limited and may not provide meaningful remedies.

Transparency alone does not necessarily lead to better decisions or fairness.

Broader governance tools can more effectively address algorithmic unfairness.

Abstract

As concerns about unfairness and discrimination in "black box" machine learning systems rise, a legal "right to an explanation" has emerged as a compellingly attractive approach for challenge and redress. We outline recent debates on the limited provisions in European data protection law, and introduce and analyze newer explanation rights in French administrative law and the draft modernized Council of Europe Convention 108. While individual rights can be useful, in privacy law they have historically unreasonably burdened the average data subject. "Meaningful information" about algorithmic logics is more technically possible than commonly thought, but this exacerbates a new "transparency fallacy"---an illusion of remedy rather than anything substantively helpful. While rights-based approaches deserve a firm place in the toolbox, other forms of governance, such as impact assessments, "soft law," judicial review, and model repositories deserve more attention, alongside catalyzing agencies acting for users to control algorithmic system design.