数字技术预测愤怒,侵略和暴力:最近的创新和方法考虑
Olivia Metcalf1, Lauren M Henry2, Catharine E Fairbairn3
1Centre for Digital Transformation of Health, University of Melbourne, VIC, Australia.
Journal of interpersonal violence
|May 29, 2025
概括
智能手机和可穿戴数据,以及人工智能/机器学习,可以预测暴力. 在对受过创伤的成年人和经历过亲密伴侣暴力的夫妇的研究中发现了方法上的挑战和伦理方面的考虑.
科学领域:
- 数字健康数字健康
- 计算社会科学 计算社会科学
- 行为预测行为预测.
背景情况:
- 预测暴力,愤怒和侵略对公共健康至关重要.
- 智能手机和可穿戴设备为行为分析提供了新的数据流.
- 对于使用此类数据来预测暴力的方法,人们的理解有限.
研究的目的:
- 报告从两项使用智能手机和可穿戴设备数据的研究中学到的方法教训.
- 探索人工智能/机器学习在预测与暴力相关的情绪和行为方面的潜力.
- 确定参与者,技术,数据和伦理挑战在这个研究领域.
主要方法:
- 两项独立研究与暴露于创伤的成年人和经历亲密伴侣暴力的成年夫妇进行了研究.
- 使用智能手机和可穿戴设备收集真实世界的数据,包括自我报告,生理和GPS数据.
- 分析的重点是利用收集的数据来预测愤怒,侵略和暴力.
主要成果:
- 面临风险的人群愿意提供关于暴力预测敏感行为的有效数据.
- 在招募和留住参与者方面遇到了重大挑战.
- 技术和数据相关问题,包括数据质量和处理,造成了困难.
- 关于数据隐私和同意的伦理考虑需要谨慎管理.
结论:
- 获得的方法见解对未来预测暴力的研究有影响.
- 尽管面临挑战,但智能手机和可穿戴设备的数据对理解和预测风险行为充满希望.
- 解决技术,数据和伦理障碍对于推动这一领域的发展至关重要.
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