FA*IR: A Fair Top-k Ranking Algorithm. Zehlike, M., Bonchi, F., Castillo, C., Hajian, S., Megahed, M., & Baeza-Yates, R. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, of CIKM '17, pages 1569–1578. ACM. event-place: Singapore, Singapore![FA*IR: A Fair Top-k Ranking Algorithm [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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 definition extends group fairness using the standard notion of protected groups and is based on ensuring that the proportion of protected candidates in every prefix 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 qualified 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.
@inproceedings{zehlike_fair:_2017,
location = {New York, {NY}, {USA}},
title = {{FA}*{IR}: A Fair Top-k Ranking Algorithm},
isbn = {978-1-4503-4918-5},
url = {http://doi.acm.org/10.1145/3132847.3132938},
doi = {10.1145/3132847.3132938},
series = {{CIKM} '17},
shorttitle = {{FA}*{IR}},
abstract = {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 definition extends group fairness using the standard notion of protected groups and is based on ensuring that the proportion of protected candidates in every prefix 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 qualified 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.},
pages = {1569--1578},
booktitle = {Proceedings of the 2017 {ACM} on Conference on Information and Knowledge Management},
publisher = {{ACM}},
author = {Zehlike, Meike and Bonchi, Francesco and Castillo, Carlos and Hajian, Sara and Megahed, Mohamed and Baeza-Yates, Ricardo},
urldate = {2019-07-10},
date = {2017},
note = {event-place: Singapore, Singapore},
keywords = {ranking, algorithmic fairness, bias in computer systems, top-k selection}
}
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