License: 
Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.FORC.2023.1
URN: urn:nbn:de:0030-drops-179224
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/17922/
Dwork, Cynthia ;
Reingold, Omer ;
Rothblum, Guy N.
From the Real Towards the Ideal: Risk Prediction in a Better World
Abstract
Prediction algorithms assign scores in [0,1] to individuals, often interpreted as "probabilities" of a positive outcome, for example, of repaying a loan or succeeding in a job. Success, however, rarely depends only on the individual: it is a function of the individual’s interaction with the environment, past and present. Environments do not treat all demographic groups equally.
We initiate the study of corrective transformations τ that map predictors of success in the real world to predictors in a better world. In the language of algorithmic fairness, letting p^* denote the true probabilities of success in the real, unfair, world, we characterize the transformations τ for which it is feasible to find a predictor q̃ that is indistinguishable from τ(p^*). The problem is challenging because we do not have access to probabilities or even outcomes in a better world. Nor do we have access to probabilities p^* in the real world. The only data available for training are outcomes from the real world.
We obtain a complete characterization of when it is possible to learn predictors that are indistinguishable from τ(p^*), in the form of a simple-to-state criterion describing necessary and sufficient conditions for doing so. This criterion is inextricably bound with the very existence of uncertainty.
BibTeX - Entry
@InProceedings{dwork_et_al:LIPIcs.FORC.2023.1,
author = {Dwork, Cynthia and Reingold, Omer and Rothblum, Guy N.},
title = {{From the Real Towards the Ideal: Risk Prediction in a Better World}},
booktitle = {4th Symposium on Foundations of Responsible Computing (FORC 2023)},
pages = {1:1--1:17},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-272-3},
ISSN = {1868-8969},
year = {2023},
volume = {256},
editor = {Talwar, Kunal},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2023/17922},
URN = {urn:nbn:de:0030-drops-179224},
doi = {10.4230/LIPIcs.FORC.2023.1},
annote = {Keywords: Algorithmic Fairness, Affirmative Action, Learning, Predictions, Multicalibration, Outcome Indistinguishability}
}
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Keywords: |
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Algorithmic Fairness, Affirmative Action, Learning, Predictions, Multicalibration, Outcome Indistinguishability |
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Collection: |
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4th Symposium on Foundations of Responsible Computing (FORC 2023) |
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Issue Date: |
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2023 |
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Date of publication: |
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04.06.2023 |
