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  The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making

Grgić-Hlača, N., Zafar, M. B., Gummadi, K. P., & Weller, A. (2016). The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making. In Symposium on Machine Learning and the Law at the 29th Conference on Neural Information Processing Systems.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-002C-EFF6-E Version Permalink: https://hdl.handle.net/11858/00-001M-0000-002C-F014-3
Genre: Conference Paper
Latex : The Case for Process Fairness in Learning: {F}eature Selection for Fair Decision Making

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http://www.mlandthelaw.org/papers/grgic.pdf (Any fulltext)
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 Creators:
Grgić-Hlača, Nina1, Author           
Zafar, Muhammad Bilal1, Author           
Gummadi, Krishna P.1, Author           
Weller, Adrian1, Author
Affiliations:
1Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society, ou_2105291              

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Language(s): eng - English
 Dates: 2016
 Publication Status: Published online
 Pages: 11 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: ZafarNIPS2016
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Title: Symposium on Machine Learning and the Law
Place of Event: Barcelona, Spain
Start-/End Date: 2016-12-08 - 2016-12-08

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Title: Symposium on Machine Learning and the Law at the 29th Conference on Neural Information Processing Systems
  Abbreviation : NIPS 2016 ML and the Law
Source Genre: Proceedings
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Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -