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 [1].
Pushing the limits of fairness impossibility: Who's the fairest of them all?
Authors: Brian Hsu, Rahul Mazumder, Preetam Nandy, Kinjal Basu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experiments demonstrating that our post-processor can improve fairness across the different definitions simultaneously with minimal model performance reduction. |
| Researcher Affiliation | Collaboration | Brian Hsu Linked In Corporation Sunnyvale, CA EMAIL Rahul Mazumder Linked In Corporation, Sunnyvale, CA (Massachusetts Institute of Technology, Cambridge, MA) EMAIL Preetam Nandy Linked In Corporation Sunnyvale, CA EMAIL Kinjal Basu Linked In Corporation Sunnyvale, CA EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper mentions utilizing an "open-source implementation" for a specific transformation method (NMDT) at https://github.com/joaquimg/QuadraticToBinary.jl, but does not state that the code for their own methodology is being released or is available. |
| Open Datasets | No | In Table 1, we take each dataset, create a 60/40 train-test split, train a grid-searched random forest model, and score the training data... We discuss the datasets and problem parameters for all experiments in the Appendix ??. The named datasets are ACS Income, ACS Insurance, ACS Mobility, ACS Poverty, ACS Coverage, ACS Travel, Heart Disease, COMPAS. However, no specific access information (links, DOIs, repositories) or formal citations with authors and year for these datasets are provided within the main paper. |
| Dataset Splits | No | In Table 1, we take each dataset, create a 60/40 train-test split, train a grid-searched random forest model, and score the training data. This explicitly mentions a train-test split but no validation split. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments were found. |
| Software Dependencies | Yes | Our MILP solution solved by Gurobi ([15]) against the QCQP problem solved by IPOPT ([21]) and the reference [15] is "Gurobi Optimization, LLC. Gurobi Optimizer Reference Manual, 2022." |
| Experiment Setup | Yes | Next, we discretize the scores into bins, parameterize the problem (# bins, ϵ, max movement, window size, solve time) on the scored training data, and compare our MILP solution solved by Gurobi ([15]) against the QCQP problem solved by IPOPT ([21]). |