  The debate on AI ethics largely focuses on technical improvements and
stronger regulation to prevent accidents or misuse of AI, with solutions
relying on holding individual actors accountable for responsible AI
development. While useful and necessary, we argue that this "agency" approach
disregards more indirect and complex risks resulting from AI's interaction with
the socio-economic and political context. This paper calls for a "structural"
approach to assessing AI's effects in order to understand and prevent such
systemic risks where no individual can be held accountable for the broader
negative impacts. This is particularly relevant for AI applied to systemic
issues such as climate change and food security which require political
solutions and global cooperation. To properly address the wide range of AI
risks and ensure 'AI for social good', agency-focused policies must be
complemented by policies informed by a structural approach.
