In recent years, discussions of responsible AI practices have seen growing support for "participatory AI" approaches, intended to involve members of the public in the design and development of AI systems. Prior research has identified a lack of standardised methods or approaches for how to use participatory approaches in the AI development process. At present, there is a dearth of evidence on attitudes to and approaches for participation in the sites driving major AI developments: commercial AI labs. Through 12 semi-structured interviews with industry practitioners and subject-matter experts, this paper explores how commercial AI labs understand participatory AI approaches and the obstacles they have faced implementing these practices in the development of AI systems and research. We find that while interviewees view participation as a normative project that helps achieve "societally beneficial" AI systems, practitioners face numerous barriers to embedding participatory approaches in their companies: participation is expensive and resource intensive, it is "atomised" within companies, there is concern about exploitation, there is no incentive to be transparent about its adoption, and it is complicated by a lack of clear context. These barriers result in a piecemeal approach to participation that confers no decision-making power to participants and has little ongoing impact for AI labs. This papers contribution is to provide novel empirical research on the implementation of public participation in commercial AI labs, and shed light on the current challenges of using participatory approaches in this context.

翻译：近年来，关于负责任人工智能实践的讨论中，“参与式人工智能”方法获得了越来越多的支持，这些方法旨在让公众参与人工智能系统的设计与开发。先前的研究指出，在人工智能开发过程中如何运用参与式方法方面缺乏标准化方法或途径。目前，在推动重大人工智能发展的场所——商业人工智能实验室中，关于参与的态度和方法的证据十分匮乏。本文通过12次半结构化访谈，与行业从业者和主题专家进行交流，探讨了商业人工智能实验室如何理解参与式人工智能方法，以及在人工智能系统和研究开发中实施这些实践所面临的障碍。我们发现，尽管受访者将参与视为有助于实现“对社会有益”的人工智能系统的规范性项目，但从业者在将参与式方法嵌入其公司时面临众多障碍：参与成本高昂且资源密集，在公司内部被“原子化”，存在对剥削的担忧，缺乏透明度激励，并且因缺乏明确背景而变得复杂。这些障碍导致参与采取零散的方式，参与者没有决策权，且对人工智能实验室的持续影响微乎其微。本文的贡献在于提供了关于商业人工智能实验室中公众参与实施的新实证研究，并揭示了在此背景下使用参与式方法当前面临的挑战。