Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features Article Swipe
Hadi Elzayn
,
Emily Black
,
Patrick Vossler
,
Nathanael Jo
,
Jacob Goldin
,
Daniel E. Ho
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.01679
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.01679
The vast majority of techniques to train fair models require access to the protected attribute (e.g., race, gender), either at train time or in production. However, in many important applications this protected attribute is largely unavailable. In this paper, we develop methods for measuring and reducing fairness violations in a setting with limited access to protected attribute labels. Specifically, we assume access to protected attribute labels on a small subset of the dataset of interest, but only probabilistic estimates of protected attribute labels (e.g., via Bayesian Improved Surname Geocoding) for the rest of the dataset. With this setting in mind, we propose a method to estimate bounds on common fairness metrics for an existing model, as well as a method for training a model to limit fairness violations by solving a constrained non-convex optimization problem. Unlike similar existing approaches, our methods take advantage of contextual information -- specifically, the relationships between a model's predictions and the probabilistic prediction of protected attributes, given the true protected attribute, and vice versa -- to provide tighter bounds on the true disparity. We provide an empirical illustration of our methods using voting data. First, we show our measurement method can bound the true disparity up to 5.5x tighter than previous methods in these applications. Then, we demonstrate that our training technique effectively reduces disparity while incurring lesser fairness-accuracy trade-offs than other fair optimization methods with limited access to protected attributes.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2310.01679
- https://arxiv.org/pdf/2310.01679
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4387355807Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2310.01679Digital Object Identifier
- Title
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Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected FeaturesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-02Full publication date if available
- Authors
-
Hadi Elzayn, Emily Black, Patrick Vossler, Nathanael Jo, Jacob Goldin, Daniel E. HoList of authors in order
- Landing page
-
https://arxiv.org/abs/2310.01679Publisher landing page
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https://arxiv.org/pdf/2310.01679Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2310.01679Direct OA link when available
- Concepts
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Computer science, Probabilistic logic, Voting, Bayesian probability, Data mining, Limit (mathematics), Geocoding, Machine learning, Artificial intelligence, Mathematics, Geology, Remote sensing, Mathematical analysis, Political science, Politics, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.empirical | 181 |
| abstract_inverted_index.estimates | 78 |
| abstract_inverted_index.important | 28 |
| abstract_inverted_index.incurring | 221 |
| abstract_inverted_index.interest, | 74 |
| abstract_inverted_index.measuring | 43 |
| abstract_inverted_index.protected | 13, 31, 55, 63, 80, 159, 164, 234 |
| abstract_inverted_index.technique | 216 |
| abstract_inverted_index.Geocoding) | 88 |
| abstract_inverted_index.attribute, | 165 |
| abstract_inverted_index.contextual | 144 |
| abstract_inverted_index.disparity. | 177 |
| abstract_inverted_index.non-convex | 132 |
| abstract_inverted_index.prediction | 157 |
| abstract_inverted_index.techniques | 4 |
| abstract_inverted_index.trade-offs | 224 |
| abstract_inverted_index.violations | 47, 127 |
| abstract_inverted_index.approaches, | 138 |
| abstract_inverted_index.attributes, | 160 |
| abstract_inverted_index.attributes. | 235 |
| abstract_inverted_index.constrained | 131 |
| abstract_inverted_index.demonstrate | 212 |
| abstract_inverted_index.effectively | 217 |
| abstract_inverted_index.information | 145 |
| abstract_inverted_index.measurement | 193 |
| abstract_inverted_index.predictions | 153 |
| abstract_inverted_index.production. | 24 |
| abstract_inverted_index.applications | 29 |
| abstract_inverted_index.illustration | 182 |
| abstract_inverted_index.optimization | 133, 228 |
| abstract_inverted_index.unavailable. | 35 |
| abstract_inverted_index.Specifically, | 58 |
| abstract_inverted_index.applications. | 209 |
| abstract_inverted_index.probabilistic | 77, 156 |
| abstract_inverted_index.relationships | 149 |
| abstract_inverted_index.specifically, | 147 |
| abstract_inverted_index.fairness-accuracy | 223 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/5 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Gender equality |
| citation_normalized_percentile |