The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Strategic Classification
Abstract
Machine learning relies on the assumption that unseen test instances of a classification problem follow the same distribution as observed training data. However, this principle can break down when machine learning is used to make important decisions about the welfare (employment, education, health) of strategic individuals. Knowing information about the classifier, such individuals may manipulate their attributes in order to obtain a better classification outcome. As a result of this behavior -- often referred to as gaming -- the performance of the classifier may deteriorate sharply. Indeed, gaming is a well-known obstacle for using machine learning methods in practice; in financial policy-making, the problem is widely known as Goodhart's law. In this paper, we formalize the problem, and pursue algorithms for learning classifiers that are robust to gaming.
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Cited by
Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018) · Philosophy & Technology · doi:10.1007/s13347-017-0279-x · Cited by 709
Actionable Recourse in Linear Classification
Ustun, B., Spangher, A., & Liu, Y. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287566 · Cited by 501
A Survey of Algorithmic Recourse: Contrastive Explanations and Consequential Recommendations
Karimi, A.-H., Barthe, G., Schölkopf, B., & Valera, I. (2023) · ACM Computing Surveys · doi:10.1145/3527848 · Cited by 301
de Laat, P. B. (2018) · Philosophy & Technology · doi:10.1007/s13347-017-0293-z · Cited by 251
How to talk when a machine is listening: Corporate disclosure in the age of AI
Cao, S., Jiang, W., Yang, B., & Zhang, A. L. (2023) · The Review of Financial Studies · doi:10.1093/rfs/hhad021 · Cited by 222
Metadata
- Authors
- Hardt, M., Megiddo, N., Papadimitriou, C., & Wootters, M.
- Venue
- Proceedings of the 2016 ACM Conference on Innovations in Theoretical Computer Science
- Publisher
- ACM
- Published
- 2016-01-14
- Pages
- 111-122
- OpenAlex
- W2913525780
- Cited by
- 223
- References
- 1
- FWCI
- 7.81
- Type
- conference paper
- Language
- en
- Open access
- closed
- Funders
- Templeton Foundation (3966)
- National Science Foundation (DMS-1400558, CCF1408635)
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- Hardt, M., Megiddo, N., Papadimitriou, C., & Wootters, M. (2016). Strategic Classification. Proceedings of the 2016 ACM Conference on Innovations in Theoretical Computer Science. https://doi.org/10.1145/2840728.2840730
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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