Responsible artificial intelligence guidelines ask engineers to consider how their systems might harm. However, contemporary artificial intelligence systems are built by composing many preexisting software modules that pass through many hands before becoming a finished product or service. How does this shape responsible artificial intelligence practice? In interviews with 27 artificial intelligence engineers across industry, open source, and academia, our participants often did not see the questions posed in responsible artificial intelligence guidelines to be within their agency, capability, or responsibility to address. We use Suchman's "located accountability" to show how responsible artificial intelligence labor is currently organized and to explore how it could be done differently. We identify cross-cutting social logics, like modularizability, scale, reputation, and customer orientation, that organize which responsible artificial intelligence actions do take place and which are relegated to low status staff or believed to be the work of the next or previous person in the imagined "supply chain." We argue that current responsible artificial intelligence interventions, like ethics checklists and guidelines that assume panoptical knowledge and control over systems, could be improved by taking a located accountability approach, recognizing where relations and obligations might intertwine inside and outside of this supply chain.

翻译：负责任人工智能指南要求工程师考虑其系统可能造成的伤害。然而，当代人工智能系统由众多预先存在的软件模块组合而成，这些模块在成为最终产品或服务之前历经多方之手。这是如何影响负责任人工智能实践的？在对来自产业界、开源社区和学术界的27位人工智能工程师进行访谈时，我们的参与者通常认为负责任人工智能指南中提出的问题超出了其能动性、能力或责任范围。我们运用Suchman的"定位责任"概念来展示当前负责任人工智能劳动的组织方式，并探讨如何以不同方式展开。我们识别出贯穿其中的社会逻辑，如模块化、规模、声誉和客户导向，这些逻辑组织着哪些负责任的AI行动得以实施，哪些被交由低级别职员或被认为应是想象中"供应链"中上下游环节的工作。我们认为，当前假设对系统具有全景式认知和控制的负责任人工智能干预措施（如伦理检查清单和指南），可通过采用定位责任方法加以改进，该方法承认该供应链内外关系与义务可能相互交织的节点。