Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
Open accessAbstract
As machine learning is increasingly used to make real-world decisions, recent research efforts aim to define and ensure fairness in algorithmic decision making. Existing methods often assume a fixed set of observable features to define individuals, but lack a discussion of certain features not being observed at test time. In this paper, we study fairness of naive Bayes classifiers, which allow partial observations. In particular, we introduce the notion of a discrimination pattern, which refers to an individual receiving different classifications depending on whether some sensitive attributes were observed. Then a model is considered fair if it has no such pattern. We propose an algorithm to discover and mine for discrimination patterns in a naive Bayes classifier, and show how to learn maximum-likelihood parameters subject to these fairness constraints. Our approach iteratively discovers and eliminates discrimination patterns until a fair model is learned. An empirical evaluation on three real-world datasets demonstrates that we can remove exponentially many discrimination patterns by only adding a small fraction of them as constraints.
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Flagged first, then by citations
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Sorted by citations
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“However, since the summation form comes from statistical query (SQ) learning, and only a few machine learning algorithms can be implemented as SQ learning, such as naïve bayes classiiers [83], support vector machines [84], and k-means clustering [85], this scheme has low applicability.”
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- Authors
- Choi, Y., Farnadi, G., Babaki, B., & Van den Broeck, G.
- Affiliations
- University of California, Los Angeles (US)
- Mila - Quebec Artificial Intelligence Institute (CA)
- Polytechnique Montréal (CA)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2020-04-03
- Volume
- 34
- Issue
- 06
- Pages
- 10077-10084
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2948288905
- Cited by
- 5
- References
- 30
- Citations by year
- 2024: 1 · 2023: 2 · 2021: 2
- FWCI
- 1.04
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- PolyPublie (École Polytechnique de Montréal) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1633857, 1837129)
- Canada First Research Excellence Fund
- Institut de Valorisation des Données
- Defense Advanced Research Projects Agency (N66001, N66001-17-2-4032)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- algorithmic fairness, Naive Bayes classifier, partial observability, sensitive attributes, fairness constraints, pattern discovery, algorithmic decision-making
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