Many selection procedures involve ordering candidates
according to their qualifications. For example, a university
might order applicants according to a perceived probability
of graduation within four years, and then select the top 1000
applicants. In this work, we address the problem of ranking
members of a population according to their “probability” of
success, based on a training set of historical binary outcome
data (e.g., graduated in four years or not). We show how to
obtain rankings that satisfy a number of desirable accuracy and
fairness criteria, despite the coarseness of the training data. As
the task of ranking is global (the rank of every individual depends
not only on their own qualifications, but also on every other
individuals’ qualifications), ranking is more subtle and vulnerable
to manipulation than standard prediction tasks.
Towards mitigating unfair discrimination caused by inaccuracies
in rankings, we develop two parallel definitions of evidence-based
rankings. The first definition relies on a semantic notion
of domination-compatibility: if the training data suggest that
members of a set S are more qualified (on average) than the
members of T, then a ranking that favors T over S (where
T dominates S) is blatantly inconsistent with the evidence, and
likely to be discriminatory. The definition asks for domination-compatibility,
not just for a pair of sets, but rather for every
pair of sets from a rich collection C of subpopulations. The
second definition aims at precluding even more general forms
of discrimination; this notion of evidence-consistency requires
that the ranking must be justified on the basis of consistency
with the expectations for every set in the collection C. Somewhat
surprisingly, while evidence-consistency is a strictly stronger
notion than domination-compatibility when the collection C is
predefined, the two notions are equivalent when the collection C
may depend on the ranking in question.
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The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric
Where machine-learned predictive risk scores inform high-stakes decisions, such as bail and sentencing in criminal justice, fairness has been a serious concern. Recent work has characterized the disparate impact that such risk scores can have when used for a binary classification task. This may not account, however, for the more diverse downstream uses of risk scores and their non-binary nature. To better account for this, in this paper, we investigate the fairness of predictive risk scores from the point of view of a bipartite ranking task, where one seeks to rank positive examples higher than negative ones. We introduce the xAUC disparity as a metric to assess the disparate impact of risk scores and define it as the difference in the probabilities of ranking a random positive example from one protected group above a negative one from another group and vice versa. We provide a decomposition of bipartite ranking loss into components that involve the discrepancy and components that involve pure predictive ability within each group. We use xAUC analysis to audit predictive risk scores for recidivism prediction, income prediction, and cardiac arrest prediction, where it describes disparities that are not evident from simply comparing within-group predictive performance.
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- Award ID(s):
- 1846210
- NSF-PAR ID:
- 10168518
- Date Published:
- Journal Name:
- Advances in neural information processing systems
- Volume:
- 32
- ISSN:
- 1049-5258
- Page Range / eLocation ID:
- 3438-3448
- 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:
- The DOI is not currently available.
