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..
Algorithmic Fairness Verification with Graphical Models
Authors: Bishwamittra Ghosh, Debabrota Basu, Kuldeep S Meel9539-9548
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically evaluate the performance of FVGM. We first present the experimental setup and the objective of our experiments, followed by experimental results. |
| Researcher Affiliation | Academia | 1 School of Computing, National University of Singapore, Singapore 2 Equipe Scool, Univ. Lille, Inria, UMR 9189 CRISt AL, CNRS, Centrale Lille, France |
| Pseudocode | No | The paper describes algorithmic steps using recursive definitions and dynamic programming but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | Yes | *Source code: https://github.com/meelgroup/justicia |
| Open Datasets | Yes | We perform the scalability analysis on five real-world datasets studied in fair ML literature: UCI Adult, German-credit (Doshi-Velez and Kim 2017), COMPAS (Angwin et al. 2016), Ricci (Mc Ginley 2010), and Titanic (https://www.kaggle.com/c/titanic). |
| Dataset Splits | Yes | We perform five-fold cross-validation on a dataset. |
| Hardware Specification | No | The computational work for this paper was performed on resources of Max Planck Institute for Software Systems, Germany and the National Supercomputing Centre, Singapore (https://www.nscc.sg). |
| Software Dependencies | No | We implement a prototype of FVGM in Python (version 3.8). We deploy the Scikit-learn library for learning linear classifiers such as Logistic Regression (LR) and Support Vector Machine (SVM) with linear kernels. For learning a Bayesian network on the converted Boolean data, we deploy the PGMPY library (Ankan and Panda 2015). |
| Experiment Setup | Yes | During discretization, we apply a gird-search to estimate the best bin-size within a maximum bin of 10. To convert the coefficients of features into integers, we employ another grid-search to choose the best multiplier within {1, 2, . . . , 100}. |