Justicia: A Stochastic SAT Approach to Formally Verify Fairness
Authors: Bishwamittra Ghosh, Debabrota Basu, Kuldeep S. Meel7554-7563
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments validate that our method is more accurate and scalable than the distributional verifiers, such as Fair Square and Veri Fair, and more robust than the sample-based empirical verifiers, such as AIF360. |
| Researcher Affiliation | Academia | Bishwamittra Ghosh1, Debabrota Basu2,3, Kuldeep S. Meel1 1 National University of Singapore, Singapore 2 Chalmers University of Technology, G oteborg, Sweden 3 Scool, Inria Lille Nord Europe, France |
| Pseudocode | Yes | Algorithm 1 Justicia: SSAT-based Fairness Verifier |
| Open Source Code | Yes | Source code: https://github.com/meelgroup/justicia |
| Open Datasets | Yes | We have experimented on multiple datasets containing multiple protected attributes: the UCI3 Adult and German-credit dataset, Pro Publica s COMPAS recidivism dataset (Angwin et al. 2016), Ricci dataset (Mc Ginley 2010), and Titanic dataset4. |
| Dataset Splits | No | The paper mentions using k-samples from the data generating distribution for error bounds in theoretical analysis but does not specify actual training/validation/test splits for the experiments conducted on the listed datasets. |
| Hardware Specification | No | The paper states that computational work was performed on resources of 'Max Planck Institute for Software Systems, Germany and the National Supercomputing Centre, Singapore', but it does not provide specific hardware details (e.g., GPU/CPU models, memory) of these resources. |
| Software Dependencies | No | We have implemented a prototype of Justicia in Python (version 3.7.3). ... We have used the Py SAT library ... for encoding the decision function of the logistic regression classifier into a CNF formula. |
| Experiment Setup | No | The paper describes the classifiers and algorithms studied (CNF learner, decision trees, logistic regression, reweighing algorithm, optimized pre-processing algorithm) and the datasets used, but it does not provide specific hyperparameter values or other system-level training settings. |