Fairness Implications of Encoding Protected Categorical Attributes-Reference-Cited by-同舟云学术

Fairness Implications of Encoding Protected Categorical Attributes

Published:2023-08-08 Issue: Volume: Page:
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Container-title:Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society
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Author:

Mougan Carlos¹^ORCID,Alvarez Jose Manuel²^ORCID,Ruggieri Salvatore³^ORCID,Staab Steffen⁴^ORCID

Affiliation:

1. Electronic and Computer Science, Univeristy of Southampton, United Kingdom

2. Computer Science, University of Pisa, Italy and Scuola Normale Superiore, Italy

3. Dipartimento di Informatica, Università di Pisa, Italy

4. Universität Stuttgart, Germany and Univeristy of Southampton, United Kingdom

Funder

European Union?s Horizon 2020

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3600211.3604657

Reference83 articles.

1. José M. Álvarez and Salvatore Ruggieri . 2023. Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference. CoRR abs/2302.11944 ( 2023 ). José M. Álvarez and Salvatore Ruggieri. 2023. Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference. CoRR abs/2302.11944 (2023).

2. Solon Barocas Moritz Hardt and Arvind Narayanan. 2019. Fairness and Machine Learning. fairmlbook.org. http://www.fairmlbook.org. Solon Barocas Moritz Hardt and Arvind Narayanan. 2019. Fairness and Machine Learning. fairmlbook.org. http://www.fairmlbook.org.

3. Big data’s disparate impact;Barocas Solon;California Law Review,2016

4. Rodrigo Kraus Barragán. 2022. Tratamiento de variables categóricas en modelos de Machine Learning. . (2022). Rodrigo Kraus Barragán. 2022. Tratamiento de variables categóricas en modelos de Machine Learning. . (2022).

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