Fetching the paper…
Reading the bibliography…
Motivated by settings in which predictive models may be required to be non-discriminatory with respect to certain attributes (such as race), but even collecting the sensitive attribute may be forbidden or restricted, we initiate the study of fair learning under the constraint of differential privacy.
Game theory, on-line prediction and boosting
Freund, Y. and Schapire, R. E. (1996) · 1996
Earlier work this paper cites.
Our data, ourselves: Privacy via distributed noise generation
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., and Naor, M. (2006a) · 2006
Earlier work this paper cites.
Mechanism design via differential privacy
McSherry, F. and Talwar, K. (2007) · 2007
Earlier work this paper cites.
Boosting and differential privacy
Dwork, C., Rothblum, G. N., and Vadhan, S. (2010) · 2010
Earlier work this paper cites.
Online learning and online convex optimization
Shalev-Shwartz, S. (2012) · 2012
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Dwork, C. and Roth, A. (2014) · 2014
Cited alongside, same era.
Equality of opportunity in supervised learning
Hardt, M., Price, E., , and Srebro, N. (2016) · 2016
Cited alongside, same era.
Is interaction necessary for distributed private learning?
Smith, A., Thakurta, A., and Upadhyay, J. (2017) · 2017
Cited alongside, same era.
Machine learning models that remember too much
Song, C., Ristenpart, T., and Shmatikov, V. (2017) · 2017
Cited alongside, same era.
Veale, M. and Binns, R. (2017) · 2017
Cited alongside, same era.
Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A. (2006b)
Cited in the paper.
An empirical study of rich subgroup fairness for machine learning
Kearns, M., Neel, S., Roth, A., and Wu, Z. S. (2018a)
Cited in the paper.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Kearns, M., Neel, S., Roth, A., and Wu, Z. S. (2018b)
Cited in the paper.
A reductions approach to fair classification
Agarwal, A., Beygelzimer, A., Dudik, M., Langford, J., and Wallach, H. (2018) · 2018
Closest in time.
The frontiers of fairness in machine learning
Chouldechova, A. and Roth, A. (2018) · 2018
Closest in time.
Blind justice: Fairness with encrypted sensitive attributes
Kilbertus, N., Gascón, A., Kusner, M. J., Veale, M., Gummadi, K. P., and Weller, A. (2018) · 2018
Closest in time.
ACM Conference on Fairness, Accountability and Transparency
ACM (2019) · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…