paper-with-me

Papers

Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina

2022-05-30 · Mohsen Abbasi, Suresh Venkatasubramanian, Sorelle A. Friedler, Kristian Lum, Calvin Barrett

Voter suppression and associated racial disparities in access to voting are long-standing civil rights concerns in the United States. Barriers to voting have taken many forms over the decades. A history of violent explicit discouragement has shifted to more subtle access limitations that can include long lines and wait times, long travel times to reach a polling station, and other logistical barriers to voting. Our focus in this work is on quantifying disparities in voting access pertaining to the overall time-to-vote, and how they could be remedied via a better choice of polling location or provisioning more sites where voters can cast ballots. However, appropriately calibrating access disparities is difficult because of the need to account for factors such as population density and different community expectations for reasonable travel times. In this paper, we quantify access to polling locations, developing a methodology for the calibrated measurement of racial disparities in polling location "load" and distance to polling locations. We apply this methodology to a study of real-world data from Florida and North Carolina to identify disparities in voting access from the 2020 election. We also introduce algorithms, with modifications to handle scale, that can reduce these disparities by suggesting new polling locations from a given list of identified public locations (including schools and libraries). Applying these algorithms on the 2020 election location data also helps to expose and explore tradeoffs between the cost of allocating more polling locations and the potential impact on access disparities. The developed voting access measurement methodology and algorithmic remediation technique is a first step in better polling location assignment.

📄 PDF Abstract BibTeX arXiv:2205.14867

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Travel 설명 없음

Similar Papers 제목 키워드 기반

Racial Disparities in Voting Wait Times: Evidence from Smartphone Data

2020-10-31

Equal access to voting is a core feature of democratic government. Using data from millions of smartphone users, we quantify a racial disparity in voting wait times across a nationwide sample of polling places during the…

"You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning

2022-06-24 · Marc Juarez, Aleksandra Korolova

As in traditional machine learning models, models trained with federated learning may exhibit disparate performance across demographic groups. Model holders must identify these disparities to mitigate undue harm to the g…

Federated Learning

Measuring Changes in Disparity Gaps: An Application to Health Insurance

2022-01-14 · Paul Goldsmith-Pinkham, Karen Jiang, Zirui Song, Jacob Wallace

We propose a method for reporting how program evaluations reduce gaps between groups, such as the gender or Black-white gap. We first show that the reduction in disparities between groups can be written as the difference…

On the Fairness Impacts of Private Ensembles Models

2023-05-19 · Cuong Tran, Ferdinando Fioretto

The Private Aggregation of Teacher Ensembles (PATE) is a machine learning framework that enables the creation of private models through the combination of multiple "teacher" models and a "student" model. The student mode…

Fairness

Diversity Matters: Revisiting Test-Time Compute in Vision-Language Models

2026-05-29 · Yijie Tong, Yifan Hou, Shaobo Cui, Antoine Bosselut 외 arxiv

Test-time compute (TTC) strategies have emerged as a lightweight approach to boost reasoning in large language models (LLMs). However, their application and benefits for vision-language models (VLMs) remain underexplored…