Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Certifying Fairness of Probabilistic Circuits
Authors: Nikil Roashan Selvam, Guy Van den Broeck, YooJung Choi
AAAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 17,956 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,288 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our discrimination pattern mining algorithms on three datasets: COMPAS which is used for recidivism prediction and the Adult (Dua and Graff 2017) and Income (Ding et al. 2021) datasets for predicting income levels. |
| Researcher Affiliation | Academia | 1 Computer Science Department, University of California, Los Angeles 2 School of Computing and Augmented Intelligence, Arizona State University |
| Pseudocode | Yes | Algorithm 1 outlines the pseudocode of our search algorithm. (...) Algorithm 2 SAMPLE-DISC-PATTERNS(C, Z) |
| Open Source Code | Yes | Link to pre-processed data, trained models, and code: https://github.com/UCLA-Star AI/PC-DiscriminationPatterns. |
| Open Datasets | Yes | We evaluate our discrimination pattern mining algorithms on three datasets: COMPAS which is used for recidivism prediction and the Adult (Dua and Graff 2017) and Income (Ding et al. 2021) datasets for predicting income levels. (...) Dua, D.; and Graff, C. 2017. UCI Machine Learning Repository. http://archive.ics.uci.edu/ml. Accessed: 2022-01-06. (...) Ding, F.; Hardt, M.; Miller, J.; and Schmidt, L. 2021. Retiring adult: New datasets for fair machine learning. Advances in Neural Information Processing Systems, 34. |
| Dataset Splits | No | The paper mentions using three benchmark datasets but does not specify the train/validation/test split percentages or methodology used for its experiments. |
| Hardware Specification | Yes | All experiments were run on an Intel(R) Xeon(R) CPU E5-2640 (2.40GHz). |
| Software Dependencies | No | The paper mentions using the STRUDEL algorithm but does not provide specific version numbers for software dependencies or libraries used. |
| Experiment Setup | No | The paper describes the algorithms and evaluation on datasets but does not provide specific hyperparameter values, training schedules, or other detailed system-level experimental setup configurations. |