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..
Improving Fairness and Privacy in Selection Problems
Authors: Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan, Somayeh Sojoudi8092-8100
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, the experiments on real-world datasets show that the exponential mechanism can improve both privacy and fairness, with a slight decrease in accuracy compared to the model without post-processing. |
| Researcher Affiliation | Academia | Mohammad Mahdi Khalili,1 Xueru Zhang, 2 Mahed Abroshan, 3 Somayeh Sojoudi 4 1 CIS Department, University of Delaware, Newark, DE, USA 2 EECS Department, University of Michigan, Ann Arbor, MI, USA 3 Alan Turing Institute, London, UK 4 EECS Department, University of California, Berkeley, CA, USA EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | No explicit pseudocode blocks or algorithm listings were found in the paper. |
| Open Source Code | No | The paper does not state that the source code for the methodology is openly available or provide a link to a repository. |
| Open Datasets | Yes | Case study 2: FICO score. We conduct two experiments using FICO credit score dataset.4 FICO scores are widely used in the United States to predict how likely an applicant is to pay back a loan. The FICO credit score dataset has been processed by Hardt et al. (Hardt, Price, and Srebro 2016) to generate CDF and non-default rate (i.e., Pr(Y = 1|R = ρ)) for different social groups (Asian, White, Hispanic, and Black). 4Find the dataset here: https://bit.ly/3di5NOC |
| Dataset Splits | No | The paper mentions using synthetic data and the FICO dataset, but it does not specify explicit train/validation/test splits for these datasets for model training or evaluation of the post-processing method. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory amounts) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper does not provide specific version numbers for any ancillary software or libraries used in the experiments. |
| Experiment Setup | No | The paper describes parameters for synthetic data generation and the FICO dataset's use but does not provide specific hyperparameters or system-level training settings for a supervised learning model, as its focus is on a post-processing step for an already trained model. |