All Politics is Local: Redistricting via Local Fairness
Authors: Shao-Heng Ko, Erin Taylor, Pankaj Agarwal, Kamesh Munagala
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we develop algorithms to audit plans for local fairness, and systematically study this concept on real-world electoral data. In particular, we study the following questions: Given a redistricting plan, can we efficiently test (or audit) whether the plan is locally fair? Are locally fair plans achievable in real redistricting tasks? If not, can we quantify how far a given plan is from being locally fair? Is local fairness empirically compatible with other existing global fairness concepts? ... In our experiments, we attempt to answer the following questions. |
| Researcher Affiliation | Academia | Shao-Heng Ko Erin Taylor Pankaj K. Agarwal Kamesh Munagala Department of Computer Science Duke University Durham, North Carolina 27708 USA Email: {sk684,ect15,pankaj,kamesh}@cs.duke.edu |
| Pseudocode | No | The paper describes algorithms (e.g., dynamic programming) using text and mathematical notation but does not provide structured pseudocode blocks or algorithm boxes. |
| Open Source Code | Yes | 3. If you ran experiments... (a) 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] ... 4. If you are using existing assets (e.g., code, data, models) or curating/releasing new assets... (c) Did you include any new assets either in the supplemental material or as a URL? [Yes] |
| Open Datasets | Yes | All data used in our experiments is obtained from the MGGG States open repository [33]. |
| Dataset Splits | No | The paper mentions generating an ensemble of 1,000 plans and specifies parameters like epsilon, but it does not provide explicit training, validation, or test dataset splits in terms of percentages or counts. The checklist states 'Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]'. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. The checklist states 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]'. |
| Software Dependencies | No | The paper mentions the use of the 'Re Com algorithm [15]' but does not provide specific software names with version numbers for dependencies required to replicate the experiment. |
| Experiment Setup | Yes | Each ensemble consists of 1, 000 redistricting plans, each being the outcome of an independent 10, 000-step Markov chain (with default population balance parameter ε = 0.02) seeded with a recent congressional electoral plan of the state. We set k to be the number of congressional districts in the 2016 election in each state. |