Envisioning Communities: A Participatory Approach Towards AI for Social Good

Elizabeth Bondi-Kelly, Lily Xu, Diana Acosta-Navas, J. Killian
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引用次数: 31

Abstract

Research in artificial intelligence (AI) for social good presupposes some definition of social good, but potential definitions have been seldom suggested and never agreed upon. The normative question of what AI for social good research should be "for" is not thoughtfully elaborated, or is frequently addressed with a utilitarian outlook that prioritizes the needs of the majority over those who have been historically marginalized, brushing aside realities of injustice and inequity. We argue that AI for social good ought to be assessed by the communities that the AI system will impact, using as a guide the capabilities approach, a framework to measure the ability of different policies to improve human welfare equity. Furthermore, we lay out how AI research has the potential to catalyze social progress by expanding and equalizing capabilities. We show how the capabilities approach aligns with a participatory approach for the design and implementation of AI for social good research in a framework we introduce called PACT, in which community members affected should be brought in as partners and their input prioritized throughout the project. We conclude by providing an incomplete set of guiding questions for carrying out such participatory AI research in a way that elicits and respects a community's own definition of social good.
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设想社区:面向社会公益的人工智能参与式方法
人工智能(AI)对社会公益的研究预设了社会公益的一些定义,但潜在的定义很少被提出,也从未达成一致。人工智能用于社会公益研究的规范问题没有经过深思熟虑的阐述,或者经常用功利主义的观点来解决,这种观点优先考虑大多数人的需求,而不是那些历史上被边缘化的人,无视不公正和不平等的现实。我们认为,人工智能的社会公益应该由人工智能系统将影响的社区进行评估,使用能力方法作为指导,这是一个衡量不同政策改善人类福利公平能力的框架。此外,我们还阐述了人工智能研究如何通过扩大和平衡能力来促进社会进步。在我们引入的PACT框架中,我们展示了能力方法如何与设计和实施人工智能用于社会公益研究的参与式方法保持一致,在该框架中,受影响的社区成员应作为合作伙伴参与,并在整个项目中优先考虑他们的投入。最后,我们提供了一套不完整的指导性问题,以引出和尊重社区自己对社会利益的定义的方式开展这种参与式人工智能研究。
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