Academic and policy proposals on algorithmic accountability often seek to understand algorithmic systems in their socio-technical context, recognising that they are produced by 'many hands'. Increasingly, however, algorithmic systems are also produced, deployed, and used within a supply chain comprising multiple actors tied together by flows of data between them. In such cases, it is the working together of an algorithmic supply chain of different actors who contribute to the production, deployment, use, and functionality that drives systems and produces particular outcomes. We argue that algorithmic accountability discussions must consider supply chains and the difficult implications they raise for the governance and accountability of algorithmic systems. In doing so, we explore algorithmic supply chains, locating them in their broader technical and political economic context and identifying some key features that should be understood in future work on algorithmic governance and accountability (particularly regarding general purpose AI services). To highlight ways forward and areas warranting attention, we further discuss some implications raised by supply chains: challenges for allocating accountability stemming from distributed responsibility for systems between actors, limited visibility due to the accountability horizon, service models of use and liability, and cross-border supply chains and regulatory arbitrage

翻译：关于算法问责的学术与政策提案通常试图在社会技术语境中理解算法系统，认识到它们是由“多方参与者”共同产生的。然而，算法系统也越来越多地在一个由多个行动者组成、通过数据流动相互连接的供应链中被生产、部署和使用。在此类情况下，正是不同行动者共同协作的算法供应链——他们贡献于系统的生产、部署、使用和功能实现——驱动着系统运行并产生特定结果。我们认为，算法问责讨论必须考虑供应链及其对算法系统的治理与问责所带来的困难影响。为此，我们探索了算法供应链，将其置于更广泛的技术与政治经济背景中，并指出了在未来关于算法治理与问责（特别是通用人工智能服务）的研究中应理解的一些关键特征。为突出前进方向与需关注的领域，我们进一步讨论了供应链引发的一些影响：因系统责任在行动者间分散导致的问责分配挑战、因问责视野造成的有限可见性、服务使用与责任模型、以及跨境供应链与监管套利问题。