Is Sortition Both Representative and Fair?
Authors: Soroush Ebadian, Gregory Kehne, Evi Micha, Ariel D. Procaccia, Nisarg Shah
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate the average-case representation of uniform selection and RANDOMREPLACE on inferred metrics derived from two demographic datasets. |
| Researcher Affiliation | Academia | Soroush Ebadian Department of Computer Science University of Toronto soroush@cs.toronto.edu Gregory Kehne School of Engineering and Applied Sciences Harvard University gkehne@g.harvard.edu Evi Micha Department of Computer Science University of Toronto emicha@cs.toronto.edu Ariel D. Procaccia School of Engineering and Applied Sciences Harvard University arielpro@seas.harvard.edu Nisarg Shah Department of Computer Science University of Toronto nisarg@cs.toronto.edu |
| Pseudocode | Yes | ALGORITHM 1: RANDOMREPLACEq |
| Open Source Code | No | The paper includes a checklist stating 'Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes]', but it does not provide a specific URL or an explicit statement in the main text about the code's public availability for the described methodology. |
| Open Datasets | Yes | Our first source of demographic data is the UCI Adult dataset, which was derived from the 1994 Current Population Survey of the US Census Bureau, and is made available by the UCI Machine Learning Repository under a CC BY 4.0 license [27]. Our second source of demographic data is the European Social Survey (ESS) [29], which is made available by the Norwegian Centre for Research Data under a CC BY 4.0 license. |
| Dataset Splits | No | The paper mentions 'data splits' in the checklist but indicates 'N/A' for specifying details. The main text does not provide specific training/validation/test splits, percentages, or sample counts for the datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. It states 'N/A' for 'total amount of compute and the type of resources used' in the checklist. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). It states 'N/A' for 'training details' in the checklist, which often includes software specifics. |
| Experiment Setup | No | The paper describes metric construction and the OPTPROXY algorithm, but it does not provide concrete hyperparameter values, training configurations, or system-level settings for the experiments. The checklist indicates 'N/A' for 'training details (e.g., data splits, hyperparameters, how they were chosen)'. |