  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
