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Counterfactual explanations are gaining prominence within technical, legal, and business circles as a way to explain the decisions of a machine learning model.
Codified at 15 u.s.c. § 1681, et seq., 1970
1970
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Codified at 15 u.s.c. § 1691, et seq., 1974
1974
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Privacy, intimacy, and personhood
Jeffrey H Reiman · 1976
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50 fed. reg. 10915, 1985
1985
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Profiling: From data to knowledge
Mireille Hildebrandt · 2006
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A taxonomy of privacy
Daniel J Solove · 2006
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Why people obey the law
Tom R. Tyler · 2006
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Privacy in context: Technology, policy, and the integrity of social life
Helen Nissenbaum · 2009
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Configuring the networked self: Law, code, and the play of everyday practice
Julie E Cohen · 2012
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What privacy is for
Julie E Cohen · 2012
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Intelligible models for classification and regression
Yin Lou, Rich Caruana, and Johannes Gehrke · 2012
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The scored society: Due process for automated predictions
Danielle Keats Citron and Frank Pasquale · 2014
Earlier work this paper cites.
Explaining data-driven document classifications
David Martens and Foster J. Provost · 2014
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Information fiduciaries and the first amendment
Jack M Balkin · 2015
Earlier work this paper cites.
On social credit and the right to be unnetworked
Nizan Geslevich Packin and Yafit Lev-Aretz · 2016
Earlier work this paper cites.
Why should I trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
https://ec.europa.eu/newsroom/article29/item-detail.cfm?item_id=612053 , 2017
Guidelines on automated individual decision-making and profiling for the purposes of regulation 2016/679 · 2017
Cited alongside, same era.
Slave to the algorithm: Why a right to an explanation is probably not the remedy you are looking for
Lilian Edwards and Michael Veale · 2017
Cited alongside, same era.
Ideas on interpreting machine learning
Patrick Hall, Wen Phan, and SriSatish Ambati · 2017
Cited alongside, same era.
The mythos of model interpretability
Zachary Lipton · 2017
Cited alongside, same era.
Why a right to legibility of automated decision-making exists in the general data protection regulation
Gianclaudio Malgieri and Giovanni Comandé · 2017
Delayed impact of fair machine learning
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt · 2018
Later among the works it cites.
The intuitive appeal of explainable machines
Andrew D. Selbst and Solon Barocas · 2018
Later among the works it cites.
Do algorithms rule the world? algorithmic decision-making and data protection in the framework of the gdpr and beyond
Maja Brkan · 2019
Closest in time.
Rethinking explainable machines: The gdpr’s right to explanation debate and the rise of algorithmic audits in enterprise
Bryan Casey, Ashkon Farhangi, and Roland Vogl · 2019
Closest in time.
The right to explanation, explained
Margot E Kaminski · 2019
Closest in time.
Model-agnostic counterfactual explanations for consequential decisions
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Cited alongside, same era.
The right not to be subject to automated decisions based on profiling
Isak Mendoza and Lee A Bygrave · 2017
Cited alongside, same era.
Meaningful information and the right to explanation
Andrew D. Selbst and Julia Powles · 2017
Cited alongside, same era.
Why a right to explanation of automated decision-making does not exist in the general data protection regulation
Sandra Wachter, Brent Mittelstadt, and Luciano Floridi · 2017
Cited alongside, same era.
Counterfactual explanations without opening the black box: Automated decisions and the gpdr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
Cited alongside, same era.
The algorithm game
Jane Bambauer and Tal Zarsky · 2018
Cited alongside, same era.
Explanations based on the missing: Towards contrastive explanations with pertinent negatives
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das · 2018
Cited alongside, same era.
Amir-Hossein Karimi, Gilles Barthe, Borja Belle, and Isabel Valera · 2019
Closest in time.
A skeptical view of information fiduciaries
Lina Khan and David Pozen · 2019
Closest in time.
How do classifiers induce agents to invest effort strategically?
Jon Kleinberg and Manish Raghavan · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
Closest in time.
Efficient search for diverse coherent explanations
Chris Russell · 2019
Closest in time.
Fairness and abstraction in sociotechnical systems
Andrew D. Selbst, danah boyd, Sorelle A Friedler, Suresh Venkatasubramanian, and Janet Vertesi · 2019
Closest in time.
Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
Closest in time.
Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind Kommiya Mothilal, Amit Sharma, and Chenhao Tan · 2020
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The philosophical basis of algorithmic recourse
Suresh Venkatasubramanian and Mark Alfano · 2020
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