A data-driven software tool for enabling cooperative information sharing among police departments

By M. Redmond, Alok Baveja. 2002

193 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Software Engineering

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Citations in the corpus

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, neutral): “0 · 101 28 61 3 Communities and Crime [63, 64] 122 1994 2.”
Fair Regression: Quantitative Definitions and Reduction-based Algorithms (2019, background): “Communities & crime : The dataset contains socio-economic, law enforcement, and crime data about communities in the US (Redmond & Baveja, 2002) with 1,994 examples.”
Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making (2019, methodology): “We evaluate our approach on the Crime dataset (Dheeru and Karra Taniskidou 2017; Redmond and Baveja 2002) with |N| = 1993 and d = 128 .”
A Convex Framework for Fair Regression (2017, background): “The Communities and Crime dataset The Communities and Crime Dataset [24, 28], from the UCI repository is a dataset which includes many features deemed relevant to violent crime rates (such as the percentage of the community’s population in an urban area, the community’s racial makeup, law…”
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods (2019, background): “Our second dataset is Communities and Crime (CC) that captures various socio-economic and law enforcement aspects, as well as crime across various communities in the US [18].”
Algorithmic Fairness (2020, background): “It is publicly available in the UCI repository [43, 115].”
Conformal prediction with conditional guarantees (2023, unknown): “Our next experiment examines the RxRx1 dataset (Taylor et al. (2019)) from the WILDS repository (Koh et al. (2021)).”
Kernel Embeddings of Latent Tree Graphical Models (2011, methodology): “Finally, we explore the performance of our method on a communities and crime dataset from the UCI repository [1, 18].”
How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods (2019, background): “Our second dataset is Communities and Crime (CC) that captures various socio-economic and law enforcement aspects, as well as crime across various communities in the US (Redmond and Baveja 2002).”
A Review on Fairness in Machine Learning (2022, background): “It is publicly available in the UCI repository [49, 59, 171].”
A variational Bayes approach to variable selection (2017, methodology): “The data collected was part of a study by Redmond and Baveja (2002) The raw data consists of 2215 samples of 147 variables the ﬁrst 5 of which we regard as non-predictive, the next 124 are regarded as potential covariates while the last 18 variables are regarded as potential response variables.”