Título:
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FA*IR: a fair top-k ranking algorithm
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Autor/a:
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Zehlike, Meike; Bonchi, Francesco; Castillo, Carlos; Hajian, Sara; Megahed, Mohamed; Baeza-Yates, Ricardo
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Abstract:
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Comunicació presentada a: CIKM '17 Conference on Information and Knowledge Management, celebrada del 6 al 10 de novembre de 2017 a Singapur, Singapur. |
Abstract:
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In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n>>k candidates, maximizing utility (i.e., select the
“best” candidates) subject to group fairness criteria.
Our ranked group fairness de nition extends group fairness using
the standard notion of protected groups and is based on ensuring
that the proportion of protected candidates in every pre x of the
top-k ranking remains statistically above or indistinguishable from
a given minimum. Utility is operationalized in two ways: (i) every
candidate included in the top-k should be more quali ed than every
candidate not included; and (ii) for every pair of candidates in the
top-k, the more qualified candidate should be ranked above.
An efficient algorithm is presented for producing the Fair Top-k
Ranking, and tested experimentally on existing datasets as well as
new datasets released with this paper, showing that our approach
yields small distortions with respect to rankings that maximize utility
without considering fairness criteria. To the best of our knowledge,
this is the first algorithm grounded in statistical tests that
can mitigate biases in the representation of an under-represented
group along a ranked list. |
Abstract:
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This research was supported by the German Research Foundation, Eurecat and the Catalonia Trade and Investment Agency (ACCIÓ). M.Z. and M.M. were supported by the GRF. C.C. and S.H. worked on this paper while at Eurecat. C.C., S.H., and F.B. were supported by ACCIÓ. |
Materia(s):
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-Algorithmic fairness -Bias in computer systems -Ranking -Top-k selection |
Derechos:
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© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in CIKM '17 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management http://doi.acm.org/10.1145/3132847.3132938 |
Tipo de documento:
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Objeto de conferencia Artículo - Versión aceptada |
Editor:
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ACM Association for Computer Machinery
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