合并公共卫生和自动化方法来解决在线仇恨言论
1University of Texas Medical Branch, 301 University Blvd, Galveston, TX 77555 USA.
概括
由于COVID-19大流行,在线仇恨言论加剧,现实世界仇恨犯罪增加. 这项研究提出了一种结合公共卫生和人工智能 (AI) 的方法,以有效打击在线仇恨言论.
科学领域:
- 公共卫生 公共卫生
- 计算机科学 计算机科学
- 社会学 社会学 社会学
背景情况:
- 随着COVID-19大流行,在线错误信息加剧,导致仇恨言论的严重程度增加.
- 在线仇恨言论已经升级为现实世界的仇恨犯罪,在2020年,美国的仇恨言论增加了32%.
- 仇恨言论由于其社会影响而造成重大公共卫生问题.
研究的目的:
- 探索网上仇恨言论的影响,并倡导将其视为公共卫生问题.
- 检查目前的人工智能 (AI) 和机器学习 (ML) 策略,以减轻仇恨言论及其道德影响.
- 提出一种综合的方法,将公共卫生措施和AI/ML结合起来,以有效地减轻仇恨言论.
主要方法:
- 对仇恨言论的影响和当前AI/ML缓解策略的文献综述.
- 分析独立公共卫生和AI/ML方法的局限性.
- 开发一种结合公共卫生和AI/ML的新综合方法.
主要成果:
- 独立的公共卫生或AI/ML策略不足以可持续地减轻仇恨言论.
- 人工智能/ML策略用于检测和减轻仇恨言论存在道德挑战.
- 综合方法利用AI/ML的反应能力和公共卫生的预防策略.
结论:
- 仇恨言论是一个关键的公共卫生问题,需要多方面的解决方案.
- 将AI/ML与公共卫生相结合,为应对网上仇恨言论提供了一个更有效和更可持续的框架.
- 拟议的综合方法旨在通过将反应性和预防性措施结合起来,减轻仇恨言论的影响.
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