Understanding accountability in algorithmic supply chains

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.

Automated summary

Discussions on algorithmic accountability need to consider the complexity of supply chains and their impact on the governance and accountability of algorithmic systems. Algorithmic supply chains, comprising multiple actors who collaborate in the production, deployment, and use of algorithms, play a crucial role in driving system functionality and generating specific outcomes. This article examines the technical and political economic context of supply chains and highlights key features that should be understood in future research on algorithmic governance and accountability.