Taking Advantage of Multitask Learning for Fair Classification
Open accessAbstract
A central goal of algorithmic fairness is to reduce bias in automated decision making. An unavoidable tension exists between accuracy gains obtained by using sensitive information as part of a statistical model, and any commitment to protect these characteristics. Often, due to biases present in the data, using the sensitive information in the functional form of a classifier improves classification accuracy. In this paper we show how it is possible to get the best of both worlds: optimize model accuracy and fairness without explicitly using the sensitive feature in the functional form of the model, thereby treating different individuals equally. Our method is based on two key ideas. On the one hand, we propose to use Multitask Learning (MTL), enhanced with fairness constraints, to jointly learn group specific classifiers that leverage information between sensitive groups. On the other hand, since learning group specific models might not be permitted, we propose to first predict the sensitive features by any learning method and then to use the predicted sensitive feature to train MTL with fairness constraints. This enables us to tackle fairness with a three-pronged approach, that is, by increasing accuracy on each group, enforcing measures of fairness during training, and protecting sensitive information during testing. Experimental results on two real datasets support our proposal, showing substantial improvements in both accuracy and fairness.
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Within sampling approaches, the application of decoupled classifiers and multitask learning has emerged.
“Within the sampling approaches, the application of decoupled classiiers and multitask learning has emerged (see [9,52,90,225,283,307]).”
From Fairness in machine learning: A survey (2024) · cites this paper as [225]
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- Authors
- Oneto, L., Doninini, M., Elders, A., & Pontil, M.
- Affiliations
- University of Genoa (IT)
- Istituto Italiano di Tecnologia, Genova, Italy
- Italian Institute of Technology (IT)
- Venue
- Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society
- Publisher
- ACM
- Published
- 2019-01-27
- Pages
- 227-237
- ISSN
- 3065-8365 (ISSN-L)
- OpenAlex
- W2895786087
- Cited by
- 64
- References
- 32
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 5 · 2023: 12 · 2022: 11 · 2021: 11 · 2020: 13 · 2019: 3
- FWCI
- 9.91
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings of the AAAI/ACM Conference on AI Ethics and Society (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (submittedVersion)
- CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council
- 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, fair classification, multi-task learning, sensitive attributes, fairness constraints, automated decision-making, bias mitigation, fairness-accuracy trade-off
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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