Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Models for Actionable Recourse
Authors: Alexis Ross, Himabindu Lakkaraju, Osbert Bastani
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of our approach with extensive experiments on real data. |
| Researcher Affiliation | Collaboration | Alexis Ross Harvard University Allen Institute for Artificial Intelligence EMAIL Himabindu Lakkaraju Harvard University EMAIL Osbert Bastani University of Pennsylvania EMAIL |
| Pseudocode | No | The paper describes the algorithm steps using text and mathematical equations but does not include a distinct pseudocode or algorithm block. |
| Open Source Code | Yes | Our code is available at https://github.com/alexisjihyeross/adversarial_recourse. |
| Open Datasets | Yes | The first contains adult income information from the 1994 United States Census Bureau [Dua and Graff, 2017]... The second contains information collected by Propublica about criminal defendants compas recidivism scores [Angwin et al., 2016]... The third dataset represents bail outcomes from two different U.S. state courts from 1990-2009 [Schmidt and Witte, 1988]... The fourth dataset is the german credit dataset [Dua and Graff, 2017] |
| Dataset Splits | Yes | We randomly split each dataset into 80% train and 20% validation sets. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., CPU, GPU models, or memory). |
| Software Dependencies | No | The paper mentions software like 'alibi implementation' and 'LIME' but does not provide specific version numbers for these or other dependencies. |
| Experiment Setup | Yes | All models are neural networks with 3 100-node hidden layers, dropout probability 0.3, and tanh activations. For evaluation, we choose the epoch achieving the highest validation F1 score. We experimented with λ values between 0.0 to 2.0 in increments of 0.2... we set δmax = 0.75 after standardizing features. |