Decision Making with Differential Privacy under a Fairness Lens
Authors: Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck, Zhiyan Yao
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed approaches are evaluated on critical decision problems that use differentially private census data.As illustrated in Figure 2, Problem PF can introduce significant disparity errors. For ϵ = 0.001, 0.01, and 0.1 the estimated fairness bounds are 0.003, 3 10 5, and 1.2 10 6 respectively, which amount to an average misallocation of $43,281, $4,328, and $865.6 respectively. |
| Researcher Affiliation | Academia | 1Syracuse University 2Georgia Institute of Technology 3Nanjing University of Science and Technology {cutran, fiorett}@syr.edu, pvh@isye.gatech.edu, zyao09@syr.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper mentions using 'differentially private census data' and refers to real-world applications based on census data, such as 'NYC census data' in figures. However, it does not provide a specific link, DOI, repository, or formal citation (with authors and year) to the exact dataset used in their experiments. |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU/CPU models, memory) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, or specialized solvers) used in the experiments. |
| Experiment Setup | Yes | For ϵ = 0.001, 0.01, and 0.1 the estimated fairness bounds are 0.003, 3 10 5, and 1.2 10 6 respectively...The estimated fairness bounds were obtained by performing a linear search over all n school districts and selecting the maximal Tr(HPF i ).obtained using m = 104 repetitions.Here T is a temperature parameter that controls the strengths of the correction. |