Bayesian Persuasion for Algorithmic Recourse
Authors: Keegan Harris, Valerie Chen, Joon Kim, Ameet Talwalkar, Hoda Heidari, Steven Z. Wu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, our numerical simulations on semi-synthetic data empirically demonstrate the benefits of using persuasion in the algorithmic recourse setting. |
| Researcher Affiliation | Academia | Keegan Harris Carnegie Mellon University keeganh@cmu.edu Valerie Chen Carnegie Mellon University valeriechen@cmu.edu Joon Sik Kim Carnegie Mellon University joonkim@cmu.edu Ameet Talwalkar Carnegie Mellon University talwalkar@cmu.edu Hoda Heidari Carnegie Mellon University hheidari@cmu.edu Zhiwei Steven Wu Carnegie Mellon University zstevenwu@cmu.edu |
| Pseudocode | Yes | We adapt the sampling-based approximation algorithm of Dughmi and Xu [13] to our setting in order to compute an -optimal and -approximate signaling policy in polynomial time, as shown in Algorithm 1 in Appendix G. |
| Open Source Code | No | The paper does not provide a concrete link or explicit statement about the availability of the source code for the methodology described. |
| Open Datasets | Yes | In this section, we provide experimental results using a semi-synthetic setting where decision subjects are based on individuals in the Home Equity Line of Credit (HELOC) dataset [15]. The HELOC dataset contains information about 9,282 customers who received a Home Equity Line of Credit. |
| Dataset Splits | No | The paper mentions using the HELOC dataset and training a logistic regression model, but does not specify any training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined splits). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments (e.g., Python, PyTorch, TensorFlow, or specific solvers). |
| Experiment Setup | Yes | In order to adapt the HELOC dataset to our strategic setting, we select four features and define five hypothetical actions A = {a;, a1, a2, a3, a4} that decision subjects may take in order to improve their observable features. Actions {a1, a2, a3, a4} result in changes to each of the decision subject s four observable features, whereas action a; does not. For simplicity, we view actions {a1, a2, a3, a4} as equally desirable to the decision maker, and assume they are all more desirable than a;. Using these four features, we train a logistic regression model that predicts whether an individual is likely to pay back a loan if given one, which will serve as the decision maker s realized assessment rule. For more information on how we constructed our experiments, see Appendix I. |