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In algorithmically fair prediction problems, a standard goal is to ensure the equality of fairness metrics across multiple overlapping groups simultaneously. We reconsider this standard fair classification problem using a probabilistic population analysis, which, in turn, reveals the Bayes-optimal classifier. Our approach unifies a variety of existing group-fair classification methods and enables extensions to a wide range of non-decomposable multiclass performance metrics and fairness measures. The Bayes-optimal classifier further inspires consistent procedures for algorithmically fair classification with overlapping groups. On a variety of real datasets, the proposed approach outperforms baselines in terms of its fairness-performance tradeoff.
Author Information
Forest Yang (UC Berkeley)
Mouhamadou M Cisse (KAUST)
Sanmi Koyejo (Illinois / Google)
Sanmi (Oluwasanmi) Koyejo an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Koyejo's research interests are in the development and analysis of probabilistic and statistical machine learning techniques motivated by, and applied to various modern big data problems. He is particularly interested in the analysis of large scale neuroimaging data. Koyejo completed his Ph.D in Electrical Engineering at the University of Texas at Austin advised by Joydeep Ghosh, and completed postdoctoral research at Stanford University with a focus on developing Machine learning techniques for neuroimaging data. His postdoctoral research was primarily with Russell A. Poldrack and Pradeep Ravikumar. Koyejo has been the recipient of several awards including the outstanding NCE/ECE student award, a best student paper award from the conference on uncertainty in artificial intelligence (UAI) and a trainee award from the Organization for Human Brain Mapping (OHBM).
More from the Same Authors
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2020 Poster: CSER: Communication-efficient SGD with Error Reset »
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2020 Poster: Fair Performance Metric Elicitation »
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2019 Poster: Learning Sparse Distributions using Iterative Hard Thresholding »
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2019 Poster: Multiclass Performance Metric Elicitation »
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2019 Tutorial: Representation Learning and Fairness »
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2016 Oral: Examples are not enough, learn to criticize! Criticism for Interpretability »
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2016 Poster: Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain »
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2016 Poster: Preference Completion from Partial Rankings »
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2016 Poster: Examples are not enough, learn to criticize! Criticism for Interpretability »
Been Kim · Sanmi Koyejo · Rajiv Khanna -
2015 Poster: Consistent Multilabel Classification »
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2014 Poster: On Prior Distributions and Approximate Inference for Structured Variables »
Sanmi Koyejo · Rajiv Khanna · Joydeep Ghosh · Russell Poldrack -
2014 Poster: Consistent Binary Classification with Generalized Performance Metrics »
Sanmi Koyejo · Nagarajan Natarajan · Pradeep Ravikumar · Inderjit Dhillon -
2014 Spotlight: Consistent Binary Classification with Generalized Performance Metrics »
Sanmi Koyejo · Nagarajan Natarajan · Pradeep Ravikumar · Inderjit Dhillon -
2014 Poster: Sparse Bayesian structure learning with dependent relevance determination prior »
Anqi Wu · Mijung Park · Sanmi Koyejo · Jonathan W Pillow
