Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation

By Sandra Wachter, Brent Daniel Mittelstadt, Luciano Floridi. 2017

1,085 citations recorded by the source corpus.

Subfields: Law, Safety Research, Sociology and Political Science, Privacy & Security, AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Law

In Safety Research

In Sociology and Political Science

In Privacy & Security

In AI Ethics, Fairness & Society

Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “In particular, conveying explanations that require non-technical expertise will be paramount to both handle ambiguities, and to develop the social right to the (not-yet available) right for explanation in the EU General Data Protection Regulation (GDPR) [355] .”
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “The question of what explanations are legally required is important and interesting, but we will leave those to legal scholars such as Bibal et al. (2020); Wachter et al. (2017).”
Fairwashing: the risk of rationalization (2019, background): “Since the right to explanation as defined in current regulations (Goodman & Flaxman, 2017) does not give precise directives on what it means to provide a “valid explanation” (Wachter et al., 2017; Edwards & Veale, 2017), there is a legal loophole that can be used by dishonest companies to cover up t”
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead (2018, background): “ an explanation is required, not an interpretable model [Goodman and Flaxman, 2016]. It is not clear whether the explanation is required to be accurate, complete, or faithful to the underlying model [Wachter et al., 2017]. Let us consider a concrete proposal moving forward. Consider a mandate that, ”
Please Stop Explaining Black Box Models for High Stakes Decisions (2018, background): “It is not clear whether the explanation is required to be accurate, complete, or faithful to the underlying model [Wachter et al., 2017].”
Local Rule-Based Explanations of Black Box Decision Systems (2018, background): “…we got trapped in a paradoxical situation in which, on one side, the legislator defines new regulations requiring that automated decisions should be explained to affected people 1 while, on the other side, even more sophisticated and obscure algorithms for decision making are generated [16, 37].”
A Survey of Methods for Explaining Black Box Models (2018, background): “Despite divergent opinions among legal scholars regarding the real scope of these clauses [36, 74, 126], there is a general agreement on the need for the implementation of such a principle is urgent and that it represents today a huge open scientific challenge.”
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach (2020, background): “As machine learning algorithms are increasingly used to assist consequential decision making in a wide range of real world settings [28, 33], providing explanations for the decision of these black-box models becomes crucial [5, 46].”
'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions (2018, background): “3The hurdle that a decision must be ‘based solely on automated processing’ is here read less restrictively than legal analysis has indicated [81, 20].”
Four Responsibility Gaps with Artificial Intelligence: Why they Matter and How to Address them (2021, background): “Also, it has been doubted that the GDPR provisions guarantee sufficient transparency and accessibility to these mechanisms for those who are subject to them (Edwards & Veale, 2017; Wachter et al., 2017).”
Privacy preservation in federated learning: An insightful survey from the GDPR perspective (2020, methodology): “Although ML-based systems are strengthened by several privacy-preserving methods, implementing these obligations in a centralised MLbased system is non-trivial, sometimes technologically impractical [119, 53].”
Trust triggers for multimodal command and control interfaces (2017, background): “Trust depends on delivery, transparency and accountability [16].”
The Fallacy of AI Functionality (2022, background): “There has been much discussion about whether the GDPR includes a "right to explanation" and what such a right entails [50, 103, 167, 193].”
Towards Transparency by Design for Artificial Intelligence (2020, methodology): “15(1)(h) GDPR) of the automated processing occurring (Casey et al. 2019; Edwards and Veale 2017, 2018; Goodman and Flaxman 2017; Kaminski 2019; Selbst and Powles 2017; Wachter et al. 2017).”
TED: Teaching AI to Explain its Decisions (2018, background): “The demand for explanation has manifested itself in new regulations that call for automated decision making systems to provide “meaningful information” on the logic used to reach conclusions (Goodman and Flaxman 2016; Wachter, Mittelstadt, and Floridi 2017b; Selbst and Powles 2017).”
The Doctor Just Won't Accept That! (2017, background): “While both the meaning and enforceability of the law and whether it amounts at all to a right to explanation remain under debate [Wachter et al., 2017, Goodman and Flaxman, 2016], machine learning papers reference the ordinance glancingly via the one paper [Goodman and Flaxman, 2016] which…”