A Canary in the AI Coal Mine: American Jews May Be Disproportionately Harmed by Intellectual Property Dispossession in Large Language Model Training
<|reference_start|>A Canary in the AI Coal Mine: American Jews May Be Disproportionately Harmed by Intellectual Property Dispossession in Large Language Model Training: Systemic property dispossession from minority groups has often been carried out in the name of technological progress. In this paper, we identify evidence that the current paradigm of large language models (LLMs) likely continues this long history. Examining common LLM training datasets, we find that a disproportionate amount of content authored by Jewish Americans is used for training without their consent. The degree of over-representation ranges from around 2x to around 6.5x. Given that LLMs may substitute for the paid labor of those who produced their training data, they have the potential to cause even more substantial and disproportionate economic harm to Jewish Americans in the coming years. This paper focuses on Jewish Americans as a case study, but it is probable that other minority communities (e.g., Asian Americans, Hindu Americans) may be similarly affected and, most importantly, the results should likely be interpreted as a "canary in the coal mine" that highlights deep structural concerns about the current LLM paradigm whose harms could soon affect nearly everyone. We discuss the implications of these results for the policymakers thinking about how to regulate LLMs as well as for those in the AI field who are working to advance LLMs. Our findings stress the importance of working together towards alternative LLM paradigms that avoid both disparate impacts and widespread societal harms.<|reference_end|>
@article{precel2024a,
    title={A Canary in the AI Coal Mine: American Jews May Be Disproportionately
  Harmed by Intellectual Property Dispossession in Large Language Model
  Training},
    author={Heila Precel, Allison McDonald, Brent Hecht, Nicholas Vincent},
    journal={arXiv preprint arXiv:2403.13073},
    year={2024},
    doi={10.1145/3613904.3642749},
    archivePrefix={arXiv},
    eprint={2403.13073},
    primaryClass={cs.CY}
}