EU regulations on algorithmic decision-making and a "right to explanation"

By Bryce Goodman, Seth R. Flaxman. 2016

211 citations recorded by the source corpus.

Subfields: AI Ethics, Fairness & Society

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Citations in the corpus

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead (2018, background): “the European Union’s revolutionary General Data Protection Regulation and other AI regulation plans govern “right to an explanation,” where only an explanation is required, not an interpretable model [36], in particular “The data subject shall have the right not to be subject to a decision based sol”
Please Stop Explaining Black Box Models for High Stakes Decisions (2018, background): “Currently the Generalized Data Protection Regulation and other AI regulation plans govern “right to an explanation," where only an explanation is required, not an interpretable model [Goodman and Flaxman, 2016].”
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 [31,101,15], everybody agrees that the need for the implementation of such a principle is urgent and that it represents today a huge open scientific challenge.”
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Policies around these concerns may eventually emerge as the activity on the policy dimensions of these biases is increasing (Crawford et al., 2016; Goodman and Flaxman, 2016; US White House, 2016).”
Learning Certifiably Optimal Rule Lists (2017, background): “There are now regulations on algorithmic decision-making in the European Union on the “right to an explanation” (Goodman and Flaxman, 2016) that would legally require interpretability in predictions.”
Causal Interpretability for Machine Learning - Problems, Methods and Evaluation (2020, background): “Nowadays, politicians, journalists and researchers are concerned regarding the interpretability of model’s decisions and whether they comply with ethical standards [31].”
Simple Rules for Complex Decisions (2017, background): “Clarity can even become a legal requirement when society demands to know how algorithmic decisions are being made [2, 13].”
Meaningful Explanations of Black Box AI Decision Systems (2019, background): “Without an enabling technology for explanation this right will either remain “dead letter”, or will just outlaw many opaque AI systems (Goodman and Flaxman 2016; Malgieri and Comand´e 2017).”
Global and Local Interpretability for Cardiac MRI Classification (2019, background): “due to a change in scanner design, imaging protocol and pre-processing, or patient demographics) and for legal reasons such as a patient’s ‘right to explanation’ [5].”
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).”
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).”
Machine Learning Interpretability: A Survey on Methods and Metrics (2019, background): “Additionally, it is necessary to better define when and what kind of explanation might be required [88,89]—what is the most appropriate explanation for each case?”