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..
Group Fairness by Probabilistic Modeling with Latent Fair Decisions
Authors: YooJung Choi, Meihua Dang, Guy Van den Broeck12051-12059
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show on real-world datasets that our approach not only is a better model of how the data was generated than existing methods but also achieves competitive accuracy. Moreover, we also evaluate our approach on a synthetic dataset in which observed labels indeed come from fair labels but with added bias, and demonstrate that the fair labels are successfully retrieved. |
| Researcher Affiliation | Academia | Yoo Jung Choi, Meihua Dang, Guy Van den Broeck Computer Science Department University of California, Los Angeles EMAIL |
| Pseudocode | No | The paper describes algorithms but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide an explicit link to source code for its methodology. The link in Appendix A points to the paper itself, not external code. |
| Open Datasets | Yes | We use three datasets: COMPAS, Adult, and German (Dua and Graff 2017), which are commonly studied benchmarks for fair ML. Dua, D.; and Graff, C. 2017. UCI Machine Learning Repository. Online Resource URL http://archive.ics.uci.edu/ml. |
| Dataset Splits | Yes | We generated different synthetic datasets with the number of non-sensitive features ranging from 10 to 30, using 10-fold CV for each. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions data generation and pre-processing steps but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, epochs) for model training. |