Explaining machine learning classifiers through diverse counterfactual explanations-Reference-Cited by-同舟云学术

Explaining machine learning classifiers through diverse counterfactual explanations

Published:2020-01-22 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency
language:
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Author:

Mothilal Ramaravind K.¹,Sharma Amit¹,Tan Chenhao²

Affiliation:

1. Microsoft Research India

2. University of Colorado Boulder

Funder

NSF

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3351095.3372850

Reference45 articles.

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3. Monica Andini Emanuele Ciani Guido de Blasio Alessio D'Ignazio and Viola Salvestrini. 2017. Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy. (2017). Monica Andini Emanuele Ciani Guido de Blasio Alessio D'Ignazio and Viola Salvestrini. 2017. Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy. (2017).

4. Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. "Machine bias: There's software used across the country to predict future criminals. And it's biased against blacks". https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. Julia Angwin Jeff Larson Surya Mattu and Lauren Kirchner. 2016. "Machine bias: There's software used across the country to predict future criminals. And it's biased against blacks". https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.

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