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When does a machine learning model predict the future of individuals and when does it recite patterns that predate the individuals? In this work, we propose a distinction between these two pathways of prediction, supported by theoretical, empirical, and normative arguments.
Calibration-based empirical probability
A. Philip Dawid · 1985
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Equality of Opportunity
John E. Roemer · 2000
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Equality of opportunity: A progress report
John E Roemer · 2002
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Confounding and confounders
Roseanne McNamee · 2003
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The reproducible properties of correct forecasts
Alvaro Sandroni · 2003
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Mediation analysis
David P MacKinnon, Amanda J Fairchild, and Matthew S Fritz · 2007
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Causality
Judea Pearl · 2009
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Epidemiology and the people’s health: theory and context
Nancy Krieger · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Prediction policy problems
Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, and Ziad Obermeyer · 2015
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Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai · 2016
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Predicting the future—big data, machine learning, and clinical medicine
Ziad Obermeyer and Ezekiel J Emanuel · 2016
Earlier work this paper cites.
Equality of opportunity: Theory and measurement
John E Roemer and Alain Trannoy · 2016
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan · 2017
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2017
Cited alongside, same era.
On individual risk
Philip Dawid · 2017
Cited alongside, same era.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
Cited alongside, same era.
Inherent trade-offs in the fair determination of risk scores
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
Cited alongside, same era.
Race after technology: Abolitionist tools for the new jim code
Ruha Benjamin · 2019
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Performative prediction
Juan Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, and Moritz Hardt · 2020
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The age of secrecy and unfairness in recidivism prediction
Cynthia Rudin, Caroline Wang, and Beau Coker · 2020
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Robustness to spurious correlations via human annotations
Megha Srivastava, Tatsunori Hashimoto, and Percy Liang · 2020
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An investigation of why overparameterization exacerbates spurious correlations
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, and Percy Liang · 2020
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It’s COMPASlicated: The messy relationship between rai datasets and algorithmic fairness benchmarks
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Avoiding discrimination through causal reasoning
Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Cited alongside, same era.
What is equality? part 1: Equality of welfare
Ronald Dworkin · 2018
Cited alongside, same era.
What is equality? part 2: Equality of resources
Ronald Dworkin · 2018
Cited alongside, same era.
Automating inequality: How high-tech tools profile, police, and punish the poor
Virginia Eubanks · 2018
Cited alongside, same era.
Multicalibration: Calibration for the (computationally-identifiable) masses
Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, and Guy N. Rothblum · 2018
Cited alongside, same era.
Michelle Bao, Angela Zhou, Samantha Zottola, Brian Brubach, Sarah Desmarais, Aaron Horowitz, Kristian Lum, and Suresh Venkatasubramanian · 2021
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Outcome indistinguishability
Cynthia Dwork, Michael P Kim, Omer Reingold, Guy N Rothblum, and Gal Yona · 2021
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Pseudo-randomness and the crystal ball
Cynthia Dwork · 2021
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Counterfactual invariance to spurious correlations in text classification
Victor Veitch, Alexander D’Amour, Steve Yadlowsky, and Jacob Eisenstein · 2021
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Parikshit Gopalan, Michael P Kim, Mihir Singhal, and Shengjia Zhao · 2022
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Counterfactual fairness is basically demographic parity
Lucas Rosenblatt and R Teal Witter · 2022
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In pursuit of interpretable, fair and accurate machine learning for criminal recidivism prediction
Caroline Wang, Bin Han, Bhrij Patel, and Cynthia Rudin · 2022
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Difficult lessons on social prediction from Wisconsin public schools
Juan C Perdomo, Tolani Britton, Moritz Hardt, and Rediet Abebe · 2023
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