评估人工智能的集体挑战:一个以人为本和公众参与的应用框架

Catarina Fontes , Christelle Al Haddad , Constantinos Antoniou , Christoph Lütge
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引用次数: 0

摘要

人工智能(AI)是一种颠覆性技术,有望彻底改变人类与城市环境之间的关系。虽然人机交互的形式正变得越来越自然和自然化,但这种交互对个人和社会的影响远非简单掌握。随着地方政府和公共当局采用人工智能和城市人工智能相关系统来支持公共服务的提供,让社区参与讨论人工智能对日常生活的影响,并就人工智能可能带来的风险和收益进行可接受的权衡,变得越来越重要。参与往往是政策制定和城市治理话语的一部分,但在城市治理中,决策者和城市决策者之间的差距并没有得到解决。此外,参与性进程的可及性和包容性所面临的障碍源于缺乏人工智能知识和对参与为何以及如何成为政策制定支柱的全面认识。我们提出了一个以人为中心和公众参与为重点的城市治理框架,用于评估人工智能的集体挑战。我们对城市治理领域的贡献是与城市代表、超政府组织和非政府组织合作设计和实施一个试点框架,并吸取各自的经验教训。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing the collective challenges of AI: An applied framework focused on people centeredness and public participation
Artificial intelligence (AI) is a disruptive technology promising to revolutionize the relationship between humans and urban environments. While the forms of human-machine interaction are becoming increasingly natural and naturalized, the impacts of such interactions for individuals and society are far from simple to grasp. As AI and Urban AI-related systems are being adopted by local governments and public authorities in support of public service delivery, it becomes increasingly important to involve communities in discussing impacts and working towards acceptable trade-offs in terms of the risks and benefits AI can represent to everyday life. Participation is often part of the discourse around policymaking and urban governance, yet the gap between decision-makers and city-makers within urban governance is not resolved. Besides, the barriers participatory processes hold to be accessible and inclusive stem now from lack of AI literacy and overall awareness on why and how participation can be a pillar of policymaking. We propose an urban governance framework for assessing the collective challenges of AI focused on people centeredness and public participation. Our contribution to the field of urban governance is a piloted framework designed and implemented in collaboration with city representatives, supra governmental organizations and non-governmental organizations and respective lessons learned.
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