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 [1].
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. |