Algorithmic Fairness Verification with Graphical Models
Authors: Bishwamittra Ghosh, Debabrota Basu, Kuldeep S Meel9539-9548
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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}. |