Maximal Combinations of Fairness Definitions
Authors: MaryBeth Defrance, Tijl De Bie
JAIR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our findings also shed light on the practical relevance and utility of each of these 12 maximal fairness definitions in various scenarios, regarding the accuracy of the classifier and ratios of false positives and false negatives, considering the base rates. ... Example of Performance Influence with The COMPAS Dataset |
| Researcher Affiliation | Academia | IDLAB, Ghent University 9000 Ghent, Belgium |
| Pseudocode | No | The paper contains extensive mathematical derivations and proofs but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, links to repositories, or mentions of code in supplementary materials. |
| Open Datasets | Yes | The COMPAS dataset (Angwin et al., 2016) is used for this example as this was used in the introduction. |
| Dataset Splits | No | The paper mentions using the COMPAS dataset and its base rates but does not provide specific training/test/validation dataset splits. It states: 'We limit ourselves to two sensitive groups, namely Caucasian and African American. The base rates are 0.43 and 0.54 respectively for the Caucasian and African American group.' |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |