A Canary in the AI Coal Mine: American Jews May Be Disproportionately Harmed by Intellectual Property Dispossession in Large Language Model Training
arxiv(2024)
摘要
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.
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