Offline Contextual Bandits with High Probability Fairness Guarantees
by Blossom Metevier, Stephen Giguere, Sarah Brockman, Ari Kobren, Yuriy Brun, Emma Brunskill, Philip Thomas
Abstract:

We present RobinHood, an offline contextual bandit algorithm designed to satisfy a broad family of fairness constraints. Our algorithm accepts multiple fairness definitions and allows users to construct their own unique fairness definitions for the problem at hand. We provide a theoretical analysis of RobinHood, which includes a proof that it will not return an unfair solution with probability greater than a user-specified threshold. We validate our algorithm on three applications: a tutoring system in which we conduct a user study and consider multiple unique fairness definitions; a loan approval setting (using the Statlog German credit data set) in which well-known fairness definitions are applied; and criminal recidivism (using data released by ProPublica). In each setting, our algorithm is able to produce fair policies that achieve performance competitive with other offline and online contextual bandit algorithms.

Citation:
Blossom Metevier, Stephen Giguere, Sarah Brockman, Ari Kobren, Yuriy Brun, Emma Brunskill, and Philip Thomas, Offline Contextual Bandits with High Probability Fairness Guarantees, in Proceedings of the 33rd Annual Conference on Neural Information Processing Systems (NeurIPS), Advances in Neural Information Processing Systems 32, 2019, pp. 14893–14904.
Bibtex:
@inproceedings{Metevier19neurips,
  author = {Blossom Metevier and Stephen Giguere and Sarah Brockman and Ari Kobren and Yuriy Brun and Emma Brunskill and Philip Thomas},
  title =
  {\href{http://people.cs.umass.edu/brun/pubs/pubs/Metevier19neurips.pdf}{Offline Contextual Bandits with High Probability Fairness Guarantees}},
  booktitle = {Proceedings of the 33rd Annual Conference on Neural
  Information Processing Systems (NeurIPS), Advances in Neural Information
  Processing Systems 32},
  venue = {NeurIPS},
  address = {Vancouver, BC, Canada},
  month = {December},
  date = {9--14},
  year = {2019},
  pages = {14893--14904},
  accept = {$\frac{1,428}{6,743} \approx 21\%$},
  url = {http://papers.neurips.cc/paper/9630-offline-contextual-bandits-with-high-probability-fairness-guarantees},

  abstract = {<p>We present RobinHood, an offline contextual bandit algorithm designed to
  satisfy a broad family of fairness constraints. Our algorithm accepts
  multiple fairness definitions and allows users to construct their own unique
  fairness definitions for the problem at hand. We provide a theoretical
  analysis of RobinHood, which includes a proof that it will not return an
  unfair solution with probability greater than a user-specified threshold. We
  validate our algorithm on three applications: a tutoring system in which we
  conduct a user study and consider multiple unique fairness definitions; a
  loan approval setting (using the Statlog German credit data set) in which
  well-known fairness definitions are applied; and criminal recidivism (using
  data released by ProPublica). In each setting, our algorithm is able to
  produce fair policies that achieve performance competitive with other
  offline and online contextual bandit algorithms.</p>},

  fundedBy = {NSF CCF-1453474, NSF IIS-1753968, NSF CCF-1763423},
}