Understand
Predictive modeling is increasingly being employed to assist human decision-makers.
- One purported advantage of replacing human judgment with computer models in high stakes settings-- such as sentencing, hiring, policing, college admissions, and parole decisions-- is the perceived "neutrality" of computers.
- It is argued that because computer models do not hold personal prejudice, the predictions they produce will be equally free from prejudice.
- There is growing recognition that employing algorithms does not remove the potential for bias, and can even amplify it, since training data were inevitably generated by a process that is itself biased.
Built on
The racial disparity in us drug arrests
Patrick A Langan · 1995
Earlier work this paper cites.
The phantom menace: Omitted variable bias in econometric research
Kevin A Clarke · 2005
Earlier work this paper cites.
Actuarial models for assessing prison violence risk revisions and extensions of the risk assessment scale for prison (rasp)
Mark D Cunningham and Jon R Sorensen · 2006
Earlier work this paper cites.
Evaluating the predictive validity of the compas risk and needs assessment system
Tim Brennan, William Dieterich, and Beate Ehret · 2009
Earlier work this paper cites.
Classifying without discriminating
Faisal Kamiran and Toon Calders · 2009
Earlier work this paper cites.
Similar
Three naive bayes approaches for discrimination-free classification
Toon Calders and Sicco Verwer · 2010
Cited alongside, same era.
The mismeasure of crime
Clayton J Mosher, Terance D Miethe, and Timothy C Hart · 2010
Cited alongside, same era.
The independence of fairness-aware classifiers
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2013
Cited alongside, same era.
Predicting human behavior
Alex Rosenblat, Tamara Kneese, et al · 2014
Cited alongside, same era.
Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
Cited alongside, same era.
Then
Discretion in hiring
Mitchell Hoffman, Lisa B Kahn, and Danielle Li · 2015
Later among the works it cites.
Interpretable classification models for recidivism prediction
Jiaming Zeng, Berk Ustun, and Cynthia Rudin · 2015
Later among the works it cites.
Machine bias
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
Closest in time.
Compas risk scales: Demonstrating accuracy equity and predictive parity
William Dieterich, Christina Mendoza, and Tim Brennan · 2016
Closest in time.
Beyond the bibliography
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…