Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses
Authors: Kaivalya Rawal, Himabindu Lakkaraju
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluation with real world datasets and user studies demonstrate that our framework can provide decision makers with a comprehensive overview of recourses corresponding to any black box model, and consequently help detect undesirable model biases and discrimination. |
| Researcher Affiliation | Academia | Kaivalya Rawal Harvard University kaivalyarawal45@gmail.com; Himabindu Lakkaraju Harvard University hlakkaraju@seas.harvard.edu |
| Pseudocode | Yes | Pseudocode for this procedure is provided in Appendix. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | Our first dataset is the COMPAS dataset which was collected by Pro Publica [18]. Our second dataset is the German Credit dataset [9]. Our third dataset is the bail decisions dataset [16]. |
| Dataset Splits | No | We split our datasets randomly into train (50%) and test sets (50%). The paper does not specify a validation set split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions using various machine learning models and baselines (e.g., LIME, SHAP, FACE, AR) but does not provide specific version numbers for software dependencies or libraries used. |
| Experiment Setup | Yes | We employ a simple tuning procedure to set the λ parameters (details in Appendix) and the constraint values are assigned as ϵ1 = 20, ϵ2 = 7, and ϵ3 = 10. Support threshold for Apriori rule mining algorithm is set to 1%. |