Unprocessing Seven Years of Algorithmic Fairness | Social Foundations of Computation – Max Planck Institute for Intelligent Systems

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Fairness and Machine Learning: Limitations and Opportunities

Unprocessing Seven Years of Algorithmic Fairness

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Unprocessing Seven Years of Algorithmic Fairness

Unprocessing3 — Example result showing the accuracy-fairness Pareto frontier achieved by postprocessing (dashed black line) contains all other methods.

This work takes a look at numerous algorithmic fairness methods published since 2016. After introducing a methodology, called unprocessing, that puts different methods on a level playing field, this computationally intensive meta study bears out a clear result: The simple “postprocessing” method of setting group specific thresholds works best.

Members

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Social Foundations of Computation

André Cruz

Doctoral Researcher

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Social Foundations of Computation

Moritz Hardt

Director

Publications

Social Foundations of Computation Conference Paper Unprocessing Seven Years of Algorithmic Fairness Cruz, A. F., Hardt, M. In The Twelfth International Conference on Learning Representations (ICLR 2024), May 2024 (Published) ArXiv Code URL BibTeX