Improving Fairness and Privacy in Selection Problems
Authors: Mohammad Mahdi Khalili, Xueru Zhang, Mahed Abroshan, Somayeh Sojoudi8092-8100
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 khalili@udel.edu, xueru@umich.edu, mabroshan@turing.ac.uk, sojoudi@berkeley.edu |
| 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. |