Explainability's Gain is Optimality's Loss? -- How Explanations Bias
Decision-making

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Explainability's Gain is Optimality's Loss? -- How Explanations Bias Decision-making

17 June 2022

Leid Zejnilovic

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Papers citing "Explainability's Gain is Optimality's Loss? -- How Explanations Bias Decision-making"

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Title
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals Sainyam Galhotra Romila Pradhan Babak Salimi CML 71 109 0 22 Mar 2021
Fairness in Machine Learning L. Oneto Silvia Chiappa FaML 299 500 0 31 Dec 2020
The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets Oana-Maria Camburu Eleonora Giunchiglia Jakob N. Foerster Thomas Lukasiewicz Phil Blunsom FAtt 69 23 0 23 Sep 2020
Problems with Shapley-value-based explanations as feature importance measures Indra Elizabeth Kumar Suresh Venkatasubramanian C. Scheidegger Sorelle A. Friedler TDI FAtt 98 368 0 25 Feb 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 571 4,391 0 23 Aug 2019
Efficient Search for Diverse Coherent Explanations Chris Russell 75 238 0 02 Jan 2019
Ít's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions Reuben Binns Max Van Kleek Michael Veale Ulrik Lyngs Jun Zhao N. Shadbolt FaML 72 546 0 31 Jan 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 293 2,271 0 24 Jun 2017
XGBoost: A Scalable Tree Boosting System Tianqi Chen Carlos Guestrin 817 39,062 0 09 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,071 0 16 Feb 2016