AI Ethics for Systemic Issues: A Structural Approach
This work highlights a gap in AI ethics for systemic problems, advocating for complementary policies to prevent indirect risks where no individual is accountable.
The paper argues that current AI ethics debates focus too much on individual accountability, overlooking systemic risks from AI's interaction with socio-economic and political contexts, and proposes a structural approach to address these broader issues, particularly in areas like climate change and food security.
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.