Counterfactual Explanations for Optimization-Based Decisions in the Context of the GDPR
Authors: Anton Korikov, Alexander Shleyfman, J. Christopher Beck
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present theoretical results for the ψ : xj m0 case. The proof can be modified to show that Theorem 2 holds for the case of ψ : xj m0 as well. |
| Researcher Affiliation | Academia | Anton Korikov , Alexander Shleyfman and J. Christopher Beck Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, Canada {korikov, jcb}@mie.utoronto.ca, shleyfman.alexander@gmail.com |
| Pseudocode | No | The paper contains theoretical results, theorems, and proofs but no structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies with datasets, therefore no training dataset availability information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies with datasets; therefore, no validation dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental procedures that would involve specific hardware; no hardware specifications are mentioned. |
| Software Dependencies | No | The paper focuses on theoretical mathematical formulations and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experiments, thus no experimental setup details such as hyperparameters or training configurations are provided. |