Fetching the paper…
Reading the bibliography…
Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness.
Causation, Prediction and Search
P. Spirtes, C. Glymour, and R. Scheines · 1993
Earlier work this paper cites.
Causality: Models, Reasoning and Inference
J. Pearl · 2000
Earlier work this paper cites.
Influence diagrams for causal modelling and inference
A. P. Dawid · 2002
Earlier work this paper cites.
What do randomized studies of housing mobility demonstrate?
M. Sobel · 2006
Earlier work this paper cites.
Classifying without discriminating
Faisal Kamiran and Toon Calders · 2009
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.
Fairness-aware classifier with prejudice remover regularizer
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma · 2012
Earlier work this paper cites.
Discrimination in online ad delivery
Latanya Sweeney · 2013
Earlier work this paper cites.
Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
Earlier work this paper cites.
Causal diagrams for interference
T. J. VanderWeele E. L. Ogburn · 2014
Earlier work this paper cites.
Censoring representations with an adversary
Harrison Edwards and Amos Storkey · 2015
Earlier work this paper cites.
Inferring network effects in relational data
D. Arbour, D. Garant, and D. Jensen · 2016
Earlier work this paper cites.
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
Cited alongside, same era.
False positives, false negatives, and false analyses: A rejoinder to machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
Anthony W Flores, Kristin Bechtel, and Christopher T Lowenkamp · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
Cited alongside, same era.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
Cited alongside, same era.
How we analyzed the compas recidivism algorithm
Jeff Larson, Surya Mattu, Lauren Kirchner, and Julia Angwin · 2016
Cited alongside, same era.
On fairness and calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger · 2017
Later among the works it cites.
When worlds collide: integrating different counterfactual assumptons in fairness
C. Russell, M. Kusner, J. Loftus, and R. Silva · 2017
Later among the works it cites.
Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive statistical relational learning approach
Shuo Yang, Mohammed Korayem, Khalifeh AlJadda, Trey Grainger, and Sriraam Natarajan · 2017
Later among the works it cites.
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna Gummadi · 2017
Later among the works it cites.
Interpretable classification models for recidivism prediction
Jiaming Zeng, Berk Ustun, and Cynthia Rudin · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Pearl, M. Glymour, and N. Jewell · 2016
Cited alongside, same era.
Estimating average causal effects under general interference, with application to a social network experiment
P. M. Aronow and C. Samii · 2017
Cited alongside, same era.
Fairness in criminal justice risk assessments: The state of the art
Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, and Aaron Roth · 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.
Avoiding discrimination through causal reasoning
Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf · 2017
Cited alongside, same era.
Counterfactual fairness
M. Kusner, J. Loftus, C. Russell, and R. Silva · 2017
Cited alongside, same era.
Statistical machine learning and data analytic methods for risk and insurance
Gareth William Peters · 2017
Cited alongside, same era.
Interventions over predictions: Reframing the ethical debate for actuarial risk assessment
Chelsea Barabas, Madars Virza, Karthik Dinakar, Joichi Ito, and Jonathan Zittrain · 2018
Closest in time.
Path-specific counterfactual fairness
S. Chiappa and T. Gillam · 2018
Closest in time.
Decoupled classifiers for group-fair and efficient machine learning
Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, and Mark DM Leiserson · 2018
Closest in time.
Causal reasoning for algorithmic fairness
J. Loftus, C. Russell, M. Kusner, and R. Silva · 2018
Closest in time.
Fair inference on outcomes
Razieh Nabi and Ilya Shpitser · 2018
Closest in time.
Fairness in decision-making: The causal explanation formula
Junzhe Zhang and Elias Bareinboim · 2018
Closest in time.