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  On Fairness, Diversity and Randomness in Algorithmic Decision Making

Grgić-Hlača, N., Zafar, M. B., Gummadi, K., & Weller, A. (2017). On Fairness, Diversity and Randomness in Algorithmic Decision Making. Retrieved from http://arxiv.org/abs/1706.10208.

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arXiv:1706.10208.pdf (Preprint), 517KB
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arXiv:1706.10208.pdf
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File downloaded from arXiv at 2018-03-19 08:30 Presented as a poster at the 2017 Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2017)
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 Urheber:
Grgić-Hlača, Nina1, Autor           
Zafar, Muhammad Bilal1, Autor           
Gummadi, Krishna1, Autor           
Weller, Adrian2, Autor
Affiliations:
1Group K. Gummadi, Max Planck Institute for Software Systems, Max Planck Society, ou_2105291              
2External Organizations, ou_persistent22              

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Schlagwörter: Statistics, Machine Learning, stat.ML,Computer Science, Learning, cs.LG
 Zusammenfassung: Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context of fairness-aware learning and demonstrate various attractive properties: (i) an ensemble of fair classifiers is guaranteed to be fair, for several different measures of fairness, (ii) an ensemble of unfair classifiers can still achieve fair outcomes, and (iii) an ensemble of classifiers can achieve better accuracy-fairness trade-offs than a single classifier. Finally, we introduce notions of distributional fairness to characterize further potential benefits of random classifier ensembles.

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Sprache(n): eng - English
 Datum: 2017-06-302017
 Publikationsstatus: Online veröffentlicht
 Seiten: 7 p.
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 Identifikatoren: arXiv: 1706.10208
URI: http://arxiv.org/abs/1706.10208
BibTex Citekey: Zafar2017arXiv
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