Themes in data mining, big data, and crime analytics

IF 6.4 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2021-10-18 DOI:10.1002/widm.1432

G. Oatley

引用次数: 17

Abstract

This article examines the impact of new AI‐related technologies in data mining and big data on important research questions in crime analytics. Because the field is so broad, the review focuses on a selection of the most important topics. Challenges for information management, and in turn law and society, include: AI‐powered predictive policing; big data for legal and adversarial decisions; bias using big data and analytics in profiling and predicting criminality; forecasting crime risk and crime rates; and, regulating AI systems.

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数据挖掘、大数据和犯罪分析的主题

本文探讨了数据挖掘和大数据中新的人工智能相关技术对犯罪分析中重要研究问题的影响。由于这个领域是如此的广泛，本评论集中在一些最重要的主题上。信息管理以及法律和社会面临的挑战包括:人工智能驱动的预测性警务;法律和对抗决策的大数据;在分析和预测犯罪行为时使用大数据和分析的偏见;预测犯罪风险和犯罪率;以及调节人工智能系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

22.70

自引率

2.60%

发文量

审稿时长

>12 weeks

期刊介绍： The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD） are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.