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Items from a database are often ranked based on a combination of criteria. The weight given to each criterion in the combination can greatly affect the fairness of the produced ranking, for example, preferring men over women. A user may have the flexibility to choose combinations that weigh these criteria differently, within limits. In this paper, we develop a system that helps users choose criterion weights that lead to greater fairness. We consider ranking functions that compute the score of each item as a weighted sum of (numeric) attribute values, and then sort items on their score. Each ranking function can be expressed as a point in a multidimensional space. For a broad range of fairness criteria, including proportionality, we show how to efficiently identify regions in this space that satisfy these criteria. Using this identification method, our system is able to tell users whether their proposed ranking function satisfies the desired fairness criteria and, if it does not, to suggest the smallest modification that does. Our extensive experiments on real datasets demonstrate that our methods are able to find solutions that satisfy fairness criteria effectively (usually with only small changes to proposed weight vectors) and efficiently (in interactive time, after some initial pre-processing).
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MithraRanking: A System for Responsible Ranking Design
Items from a database are often ranked based on a combination of criteria. The weight given to each criterion in the combination can greatly affect the ranking produced. Often, a user may have a general sense of the relative importance of the different criteria, but beyond this may have the flexibility, within limits, to choose combinations that weigh these criteria differently with an acceptable region. We demonstrate MithraRanking, a system that helps users choose criterion weights that lead to “better” rankings in terms of having desirable properties while remaining within the acceptable region. The goodness properties we focus on are stability and fairness.
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- PAR ID:
- 10108007
- Date Published:
- Journal Name:
- Proc. ACM SIGMOD Intl Conf on Management of Data
- Page Range / eLocation ID:
- 1913 to 1916
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3299869.3320244
