Offline Contextual Bandits with High Probability Fairness Guarantees
Authors: Blossom Metevier, Stephen Giguere, Sarah Brockman, Ari Kobren, Yuriy Brun, Emma Brunskill, Philip S. Thomas
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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 Pro Publica. 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. |
| Researcher Affiliation | Academia | 1College of Information and Computer Sciences 2Computer Science Department University of Massachusetts Amherst Stanford University |
| Pseudocode | Yes | Algorithm 1 Robin Hood (D, = {δi}k i=1, ˆZ{ˆzi j}d j=1, E = {Ei}k i=1) ... Algorithm 2 Candidate Utility(θ, Dc, , ˆZ, E) |
| Open Source Code | No | The paper does not provide a direct link to open-source code for the Robin Hood algorithm or explicitly state that the code is being released. |
| Open Datasets | Yes | We use the Statlog German Credit data set, which includes a collection of loan applicants, each one described by 20 features, and labels corresponding to whether or not each applicant was assessed to have good financial credit [32]. ... This experiment uses recidivism data released by Pro Publica as part of their investigation into the racial bias of deployed classification algorithms [3]. |
| Dataset Splits | No | The paper describes partitioning data into 'candidate selection set Dc' and 'safety set Ds' within the algorithm (Algorithm 1), but it does not specify standard training, validation, and test dataset splits with percentages or sample counts for external reproducibility of the overall evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or specific cluster configurations) used to run the experiments. |
| Software Dependencies | No | The paper mentions using 'Student s t-test' and 'CMA-ES [19]' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | No | The paper mentions using 'CMA-ES [19] as our optimization method' but does not specify its hyperparameters or other detailed training configurations (e.g., learning rates, batch sizes, epochs) for the experiments. |