Fetching the paper…

Reading the bibliography…

2017

Paradoxes in Fair Computer-Aided Decision Making

Morgan, Andrew, Pass, Rafael

Original
PDF
Post on X

Understand

Computer-aided decision making--where a human decision-maker is aided by a computational classifier in making a decision--is becoming increasingly prevalent.

For instance, judges in at least nine states make use of algorithmic tools meant to determine "recidivism risk scores" for criminal defendants in sentencing, parole, or bail decisions.
A subject of much recent debate is whether such algorithmic tools are "fair" in the sense that they do not discriminate against certain groups (e.g., races) of people.
Our main result shows that for "non-trivial" computer-aided decision making, either the classifier must be discriminatory, or a rational decision-maker using the output of the classifier is forced to be discriminatory.

Paradoxes in Fair Computer-Aided Decision Making · Around

Built on

Games with incomplete information played by Bayesian players, i-iii
John C. Harsanyi · 1968
Earlier work this paper cites.
Direct and indirect effects
Judea Pearl · 2001
Earlier work this paper cites.
Differential privacy
Cynthia Dwork · 2006
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Earlier work this paper cites.
Big Data: Seizing Opportunities, Preserving Values
John Podesta, Penny Pritzker, Ernest J. Moniz, John Holdren, and Jefrey Zients · 2014
Earlier work this paper cites.

Similar

How we analyzed the COMPAS recidivism algorithm
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
Cited alongside, same era.
Machine bias: Risk assessments in criminal sentencing
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
Cited alongside, same era.
Big data’s disparate impact
Solon Barocas and Andrew Selbst · 2016
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2016
Cited alongside, same era.

Then

Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2016
Later among the works it cites.
Fairness beyond disparate treatment and disparate impact: Learning classification without disparate mistreatment
Original
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P. Gummadi · 2016
Later among the works it cites.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
Closest in time.
Achieving fair treatment in algorithmic classification
Andrew Morgan and Rafael Pass · 2017
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…