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

By Lilian Edwards, Michael Veale. 2018

156 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Law, Safety Research

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Citations in the corpus

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward (2020, background): “The France’s Digital Republic Act gives the right to an explanation as regards decisions on an individual made through the use of administrative algorithms (Edwards and Veale, 2018).”
Do Not Trust Additive Explanations (2019, background): “…of models has been a hot topic in the last year, both in the Machine Learning community (Gill and Hall, 2018) as well as in the legal community that coined the phrase „Right to Explanation” in the discussion around General Data Protection Regulation (Wachter et al., 2017; Edwards and Veale, 2018).”
Legal requirements on explainability in machine learning (2020, background): “The majority of scholars support this requirement of explanation (Goodman and Flaxman 2016; Malgieri and Comandé 2017; Edwards and Veale 2018; Selbst and Powles 2017).”
Interpretable Machine Learning (2019, background): “13 79 Some have called for the law to be strengthened to include the right to an explanation in cases where ML is part of a decision.”
A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis (2019, methodology): “This even further highlights the need for transparent algorithms which we can reason about [Goodman and Flaxman (2017), Edwards and Veale (2017a), Edwards and Veale (2017b)].”
From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices (2019, background): “…& Guestrin, 2016), SHAP (Lundberg & Lee, 2017), Sensitivity Analysis (Oxborough and colleagues, 2018)—do not really succeed in helping developers provide meaningful (Edwards & Veale, 2018) explanations that give individuals greater control over what is being inferred about them from their data.”
Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns (2019, background): “Making information open and transparent requires the individuals aﬀected to be literate in assessing the risks of AI and automated decision-making systems and puts the onus on them to challenge automated decisions (Edwards and Veale, 2018).”