HARE: Human-in-the-Loop Algorithmic Recourse
Authors: Sai Srinivas Kancheti, Rahul Vigneswaran, Bamdev Mishra, Vineeth N. Balasubramanian
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments on 3 benchmark datasets on top of 6 popular baseline recourse methods where we observe that our framework performs significantly better on simulated user preferences. |
| Researcher Affiliation | Collaboration | Sai Srinivas Kancheti EMAIL Indian Institute of Technology Hyderabad, India Rahul Vigneswaran EMAIL Indian Institute of Technology Hyderabad, India Bamdev Mishra EMAIL Microsoft, India Vineeth N Balasubramanian EMAIL Indian Institute of Technology Hyderabad, India |
| Pseudocode | Yes | Algorithm 1: Actionable Sampling Algorithm 2: Boundary Point Search Algorithm 3: Final Candidate Recourses Algorithm 4: HARE |
| Open Source Code | Yes | Our code is publicly available . https://github.com/rahulvigneswaran/HARE |
| Open Datasets | Yes | Datasets. We evaluate on 3 commonly used binary datasets spanning different application domains including credit worthiness, criminal recidivism, and income prediction, which are popularly used in recourse literature. Adult Income Becker & Kohavi (1996) is a binary classification dataset... Give Me Some Credit Kaggle (2021) is used to predict credit worthiness... Finally we consider COMPAS Larson et al. (2016) |
| Dataset Splits | No | The paper mentions using a 'test-set' for recourse generation and '150 fixed individual samples taken from the test-set', but does not explicitly state the training/validation/test split percentages or methodology for the datasets used to train the classifiers. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or cloud computing specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'CARLA Pawelczyk et al. (2021)' for recourse generator implementations and the 'Adam Kingma & Ba (2014) optimizer', but it does not specify version numbers for these or other software libraries/dependencies. |
| Experiment Setup | Yes | We have a total budget of B = 30 user queries... For Actionable Sampling, we perform full-batch gradient descent using the Adam Kingma & Ba (2014) optimizer for n = 100 iterations with a learning-rate of 0.1. We set the magnitude hyperparameter γ to 1, and the regularization hyperparameter λ to 10. In Boundary Point Search the tolerance value ϵ is set to 1e 06. All experimental results are averaged over 5 seeds to ensure robustness. |