Mapping the individual, social, and biospheric impacts of Foundation Models

TL;DR

The paper tackles the gap between real-world harms and speculative existential risks in foundation models by proposing an integrative socio-technical framework that maps 14 risks across individual, social, and biospheric levels. Using an abductive literature review that combines snowball sampling and structured database search, the authors analyze 167 papers (2018–2023) to categorize harms and illustrate interdependencies across levels. The main contributions are the risk taxonomy and the argument for governance that goes beyond safety-focused narratives to address social justice, labor, and environmental dimensions, with practical recommendations for technical and normative interventions. This approach provides a more holistic basis for responsible AI, urging policymakers and researchers to consider cross-cutting impacts in policy design and technology development.

Abstract

Responding to the rapid roll-out and large-scale commercialization of foundation models, large language models, and generative AI, an emerging body of work is shedding light on the myriad impacts these technologies are having across society. Such research is expansive, ranging from the production of discriminatory, fake and toxic outputs, and privacy and copyright violations, to the unjust extraction of labor and natural resources. The same has not been the case in some of the most prominent AI governance initiatives in the global north like the UK's AI Safety Summit and the G7's Hiroshima process, which have influenced much of the international dialogue around AI governance. Despite the wealth of cautionary tales and evidence of algorithmic harm, there has been an ongoing over-emphasis within the AI governance discourse on technical matters of safety and global catastrophic or existential risks. This narrowed focus has tended to draw attention away from very pressing social and ethical challenges posed by the current brute-force industrialization of AI applications. To address such a visibility gap between real-world consequences and speculative risks, this paper offers a critical framework to account for the social, political, and environmental dimensions of foundation models and generative AI. We identify 14 categories of risks and harms and map them according to their individual, social, and biospheric impacts. We argue that this novel typology offers an integrative perspective to address the most urgent negative impacts of foundation models and their downstream applications. We conclude with recommendations on how this typology could be used to inform technical and normative interventions to advance responsible AI.