The use of algorithmic decision making systems in domains which impact the financial, social, and political well-being of people has created a demand for these to be “fair” under some accepted notion of equity. This demand has in turn inspired a large body of work focused on the development of fair learning algorithms which are then used in lieu of their conventional counterparts. Most analysis of such fair algorithms proceeds from the assumption that the people affected by the algorithmic decisions are represented as immutable feature vectors. However, strategic agents may possess both the ability and the incentive to manipulate this observed feature vector in order to attain a more favorable outcome. We explore the impact that strategic agent behavior can have on group-fair classification. We find that in many settings strategic behavior can lead to fairness reversal, with a conventional classifier exhibiting higher fairness than a classifier trained to satisfy group fairness. Further, we show that fairness reversal occurs as a result of a group- fair classifier becoming more selective, achieving fairness largely by excluding individuals from the advantaged group. In contrast, if group fairness is achieved by the classifier becoming more inclusive, fairness reversal does not occur.
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Fairness Under Composition
Algorithmic fairness, and in particular the fairness of scoring and classification algorithms, has
become a topic of increasing social concern and has recently witnessed an explosion of research in
theoretical computer science, machine learning, statistics, the social sciences, and law. Much of
the literature considers the case of a single classifier (or scoring function) used once, in isolation.
In this work, we initiate the study of the fairness properties of systems composed of algorithms
that are fair in isolation; that is, we study fairness under composition. We identify pitfalls
of naïve composition and give general constructions for fair composition, demonstrating both
that classifiers that are fair in isolation do not necessarily compose into fair systems and also
that seemingly unfair components may be carefully combined to construct fair systems. We
focus primarily on the individual fairness setting proposed in [Dwork, Hardt, Pitassi, Reingold,
Zemel, 2011], but also extend our results to a large class of group fairness definitions popular in
the recent literature, exhibiting several cases in which group fairness definitions give misleading
signals under composition.
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- Award ID(s):
- 1763665
- NSF-PAR ID:
- 10217367
- Editor(s):
- Blum, A
- Date Published:
- Journal Name:
- 10th Innovations in Theoretical Computer Science Conference (ITCS 2019)
- ISSN:
- 1868-8969
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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