AI Ethics for Systemic Issues: A Structural Approach

Individual accountability alone is insufficient: The paper argues that focusing only on holding individual AI developers and companies responsible misses systemic risks where "no individual can be held accountable for the broader negative impacts"—suggesting that current regulatory approaches may create a false sense of security.

AI can amplify existing societal problems: When AI is applied to complex issues like climate change and food security, its effects interact with broader socio-economic and political systems in ways that individual actors cannot control or prevent, requiring structural policy solutions rather than just technical fixes.

Systemic risks require systemic solutions: The authors contend that preventing negative outcomes from AI requires complementary policies informed by a "structural approach" that addresses how AI interacts with society as a whole, not just policies focused on individual developer behavior and stronger regulation.

Current AI ethics debate is incomplete: The paper identifies a significant gap in mainstream AI ethics discussions, which tend to emphasize technical improvements and accident prevention but overlook "more indirect and complex risks" that emerge from AI's role within larger socio-economic and political contexts.

"AI for social good" demands broader thinking: Achieving genuinely beneficial AI outcomes for global challenges requires moving beyond the traditional "agency" framework to consider how political solutions and international cooperation must shape AI policy alongside individual responsibility measures.