Fetching the paper…
Reading the bibliography…
Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and prison sentencing.
Scaling up the accuracy of naive-Bayes classifiers: a decision-tree hybrid
Kohavi, R · 1996
Earlier work this paper cites.
Three naive Bayes approaches for discrimination-free classification
Calders, T. and Verwer, S · 2010
Earlier work this paper cites.
Data preprocessing techniques for classification without discrimination
Kamiran, F. and Calders, T · 2012
Earlier work this paper cites.
Decision theory for discrimination-aware classification
Kamiran, F., Karim, A., and Zhang, X · 2012
Earlier work this paper cites.
Fairness-aware classifier with prejudice remover regularizer
Kamishima, T., Akaho, S., Asoh, H., and Sakuma, J · 2012
Earlier work this paper cites.
Learning fair representations
Zemel, R., Wu, Y. L., Swersky, K., Pitassi, T., and Dwork, C · 2013
Earlier work this paper cites.
A data-driven approach to predict the success of bank telemarketing
Moro, S., Cortez, P., and Rita, P · 2014
Earlier work this paper cites.
Medical Expenditure Panel Survey data: 2015 Full Year Consolidated Data File, 2015
AHRQ · 2015
Earlier work this paper cites.
Certifying and removing disparate impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., and Venkatasubramanian, S · 2015
Earlier work this paper cites.
”FairML : Toolbox for diagnosing bias in predictive modeling”
Adebayo, J. A · 2016
Earlier work this paper cites.
Medical Expenditure Panel Survey data: 2016 Full Year Consolidated Data File, 2016
AHRQ · 2016
Earlier work this paper cites.
Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks
Angwin, J., Larson, J., Mattu, S., and Kirchner, L · 2016
Earlier work this paper cites.
COMPAS risk scales: Demonstrating accuracy equity and predictive parity, 2016
Dieterich, W., Mendoza, C., and Brennan, T · 2016
Cited alongside, same era.
On the (im)possibility of fairness
Friedler, S. A., Scheidegger, C., and Venkatasubramanian, S · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
Cited alongside, same era.
Technical response to Northpointe, 2016
Larson, J. and Angwin, J · 2016
Cited alongside, same era.
How we analyzed the COMPAS recidivism algorithm, 2016
Larson, J., Mattu, S., Kirchner, L., and Angwin, J · 2016
Cited alongside, same era.
Optimized pre-processing for discrimination prevention
Calmon, F. P., Wei, D., Vinzamuri, B., Natesan Ramamurthy, K., and Varshney, K. R · 2017
On fairness and calibration
Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., and Weinberger, K. Q · 2017
Later among the works it cites.
FairTest: Discovering unwarranted associations in data-driven applications
Tramèr, F., Atlidakis, V., Geambasu, R., Hsu, D., Hubaux, J.-P., Humbert, M., Juels, A., and Lin, H · 2017
Later among the works it cites.
Fairness Measures: Datasets and software for detecting algorithmic discrimination, 2017
Zehlike, M., Castillo, C., Bonchi, F., Hajian, S., and Megahed, M · 2017
Later among the works it cites.
Themis-ml: A fairness-aware machine learning interface for end-to-end discrimination discovery and mitigation
Bantilan, N · 2018
Closest in time.
A comparative study of fairness-enhancing interventions in machine learning
Friedler, S. A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E. P., and Roth, D · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Conscientious classification: A data scientist’s guide to discrimination-aware classification
d’Alessandro, B., O’Neil, C., and LaGatta, T · 2017
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru, D. and Karra Taniskidou, E · 2017
Cited alongside, same era.
Fairness Testing: Testing software for discrimination
Galhotra, S., Brun, Y., and Meliou, A · 2017
Cited alongside, same era.
Inherent trade-offs in the fair determination of risk scores
Kleinberg, J., Mullainathan, S., and Raghavan, M · 2017
Cited alongside, same era.
Counterfactual fairness
Kusner, M. J., Loftus, J. R., Russell, C., and Silva, R · 2017
Cited alongside, same era.
A large-scale analysis of racial disparities in police stops across the united states
Pierson, E., Simoiu, C., Overgoor, J., Corbett-Davies, S., Ramachandran, V., Phillips, C., and Goel, S · 2017
Cited alongside, same era.
Welfare and distributional impacts of fair classification
Hu, L. and Chen, Y · 2018
Closest in time.
Translation tutorial: 21 fairness definitions and their politics
Narayanan, A · 2018
Closest in time.
A unified approach to quantifying algorithmic unfairness: Measuring individual & group unfairness via inequality indices
Speicher, T., Heidari, H., Grgic-Hlaca, N., Gummadi, K. P., Singla, A., Weller, A., and Zafar, M. B · 2018
Closest in time.
Aequitas: Bias and fairness audit, 2018
Stevens, A., Anisfeld, A., Kuester, B., London, J., Saleiro, P., and Ghani, R · 2018
Closest in time.
Actionable recourse in linear classification
Ustun, B., Spangher, A., and Liu, Y · 2018
Closest in time.
Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Wachter, S., Mittelstadt, B., and Russell, C · 2018
Closest in time.
Mitigating unwanted biases with adversarial learning
Zhang, B. H., Lemoine, B., and Mitchell, M · 2018
Closest in time.