Equality of Opportunity in Classification: A Causal Approach
Authors: Junzhe Zhang, Elias Bareinboim
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our results through experiments in both real (COMPAS) and synthetic datasets. and 6 Simulations and Experiments |
| Researcher Affiliation | Academia | Junzhe Zhang Purdue University, USA zhang745@purdue.edu Elias Bareinboim Purdue University, USA eb@purdue.edu |
| Pseudocode | Yes | Algorithm 1: Find Exp Set; Algorithm 3: Ctf-Fair Learning; Algorithm 2: Causal-SFFS |
| Open Source Code | No | The paper does not include any statement about releasing source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We demonstrate our results through experiments in both real (COMPAS) and synthetic datasets. and [1] J. Angwin, J. Larson, S. Mattu, and L. Kirchner. Machine bias: There s software used across the country to predict future criminals. and it s biased against blacks. Pro Publica, 23, 2016. |
| Dataset Splits | No | The paper mentions 'validation data' for evaluating predictive accuracy during feature selection ('evaluating the best in-class predictive accuracy for classifiers in { f : ˆ PA ˆY } on the validation data.'), but it does not specify concrete dataset split percentages or counts for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud resources) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., programming language versions, library versions, or solver versions) used in the experiments. |
| Experiment Setup | No | The paper states that 'Details of the experiments are provided in Appendix C [27]', but this paper itself does not contain specific hyperparameters, training configurations, or system-level settings within its main text. |