Name
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 user study with an automated tutoring system, a loan approval setting using the Statlog German credit data set, and a criminal recidivism problem using data released by ProPublica. To demonstrate the versatility of our approach, we use multiple well-known and custom definitions of fairness. In each setting, our algorithm is able to produce fair policies that achieve performance competitive with other offline and online contextual bandit algorithms. |
Year | Venue | Keywords |
|---|---|---|
2019 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019) | user study |
Field | DocType | Volume |
Computer science,Artificial intelligence,Machine learning | Conference | 32 |
ISSN | Citations | PageRank |
1049-5258 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Blossom Metevier | 1 | 0 | 0.68 |
| Stephen Giguere | 2 | 101 | 6.72 |
| Brockman, Sarah | 3 | 0 | 0.34 |
| Ari Kobren | 4 | 28 | 5.17 |
| Yuriy Brun | 5 | 2216 | 91.89 |
| Emma Brunskill | 6 | 673 | 90.33 |
| Philip S. Thomas | 7 | 184 | 22.55 |