Mapping the individual, social, and biospheric impacts of Foundation Models
<|reference_start|>Mapping the individual, social, and biospheric impacts of Foundation Models: 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.<|reference_end|>
@article{hernández2024mapping,
    title={Mapping the individual, social, and biospheric impacts of Foundation
  Models},
    author={Andr'es Dom'inguez Hern'andez, Shyam Krishna, Antonella Maia
  Perini, Michael Katell, SJ Bennett, Ann Borda, Youmna Hashem, Semeli
  Hadjiloizou, Sabeehah Mahomed, Smera Jayadeva, Mhairi Aitken, David Leslie},
    journal={In Proceedings of the 2024 ACM Conference on Fairness,
  Accountability, and Transparency (FAccT '24). Association for Computing
  Machinery, New York, NY, USA, 776-796},
    year={2024},
    doi={10.1145/3630106.3658939},
    archivePrefix={arXiv},
    eprint={2407.17129},
    primaryClass={cs.CY}
}