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.

中文翻译：

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构想社区：以人工智能促进社会公益的参与式方法

针对社会公益的人工智能（AI）研究以社会公益的一些定义为前提，但是很少提出潜在的定义，也从未达成共识。应该为社会公益研究“使用什么人工智能”的规范性问题没有经过深思熟虑地阐述，或者常常以功利主义的观点来解决，这种观点优先考虑了大多数人的需求，而不是那些历史上处于边缘地位的人，而忽视了不公正和不平等的现实。 。我们认为，人工智能对社会福利的影响应由人工智能系统将影响的社区进行评估，以能力方法为指导，以衡量不同政策改善人类福利公平能力的框架为基础。此外，我们提出了AI研究如何通过扩展和均衡能力来促进社会进步的潜力。我们介绍了在称为PACT的框架中，能力方法如何与参与式方法一起设计和实施用于社会公益研究的AI，其中受影响的社区成员应作为合作伙伴加入，并在整个项目中优先考虑他们的投入。最后，我们提供了一组不完整的指导性问题，以引发和尊重社区自己对社会福利的定义的方式来进行此类参与性AI研究。其中，受影响的社区成员应作为合作伙伴加入，并在整个项目中优先考虑他们的意见。最后，我们提供了一组不完整的指导性问题，以引发和尊重社区自己对社会福利的定义的方式来进行此类参与性AI研究。其中，受影响的社区成员应作为合作伙伴加入，并在整个项目中优先考虑他们的意见。最后，我们提供了一组不完整的指导性问题，以引发和尊重社区自己对社会福利的定义的方式来进行此类参与性AI研究。