Addressing Under-Reporting to Enhance Fairness and Accuracy in Mobility-based Crime Prediction-Reference-Cited by-同舟云学术

Addressing Under-Reporting to Enhance Fairness and Accuracy in Mobility-based Crime Prediction

Published:2020-11-03 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th International Conference on Advances in Geographic Information Systems
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Author:

Wu Jiahui¹,Frias-Martinez Enrique²,Frias-Martinez Vanessa³

Affiliation:

1. College of Information Studies, University of Maryland, College Park, Maryland, USA

2. Telefonica Research, Madrid, Spain

3. College of Information Studies UMIACS, University of Maryland, College Park, Maryland, USA

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3397536.3422205

Reference39 articles.

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2. Richard Berk Hoda Heidari Shahin Jabbari Matthew Joseph Michael Kearns Jamie Morgenstern Seth Neel and Aaron Roth. 2017. A Convex Framework for Fair Regression. (June 2017). arXiv:cs.LG/1706.02409 Richard Berk Hoda Heidari Shahin Jabbari Matthew Joseph Michael Kearns Jamie Morgenstern Seth Neel and Aaron Roth. 2017. A Convex Framework for Fair Regression. (June 2017). arXiv:cs.LG/1706.02409

3. Moves on the Street: Classifying Crime Hotspots Using Aggregated Anonymized Data on People Dynamics

4. General Methods for Monitoring Convergence of Iterative Simulations

5. The Compact City: Just or Just Compact;Burton Elizabeth;A Preliminary Analysis. Urban Stud.,2000

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