The Psychosocial Impacts of Generative AI Harms
<|reference_start|>The Psychosocial Impacts of Generative AI Harms: The rapid emergence of generative Language Models (LMs) has led to growing concern about the impacts that their unexamined adoption may have on the social well-being of diverse user groups. Meanwhile, LMs are increasingly being adopted in K-20 schools and one-on-one student settings with minimal investigation of potential harms associated with their deployment. Motivated in part by real-world/everyday use cases (e.g., an AI writing assistant) this paper explores the potential psychosocial harms of stories generated by five leading LMs in response to open-ended prompting. We extend findings of stereotyping harms analyzing a total of 150K 100-word stories related to student classroom interactions. Examining patterns in LM-generated character demographics and representational harms (i.e., erasure, subordination, and stereotyping) we highlight particularly egregious vignettes, illustrating the ways LM-generated outputs may influence the experiences of users with marginalized and minoritized identities, and emphasizing the need for a critical understanding of the psychosocial impacts of generative AI tools when deployed and utilized in diverse social contexts.<|reference_end|>
@article{vassel2024the,
    title={The Psychosocial Impacts of Generative AI Harms},
    author={Faye-Marie Vassel, Evan Shieh, Cassidy R. Sugimoto, Thema Monroe-White},
    journal={arXiv preprint arXiv:2405.01740},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01740},
    primaryClass={cs.CL}
}