Record summary
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Risk profile
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"Rapid advances in LLMs pose three distinct sets of challenges for workers’ incomes (Korinek and Stiglitz, 2019; Susskind, 2023). First, they are likely to accelerate the rate of job turnover and disruption —– affecting more workers, including more highly skilled workers, and making the adjustment process for society more difficult than what we were used to from prior technological advances...Second, although technological progress means that society may produce more wealth overall, there is a risk that the general-purpose nature of LLMs may lead to progress that is biased against labor, meaning that the share of that wealth that goes to labor may decline...Third, if future LLMs and robots advance to the point where they can perform virtually all the work tasks, they would disrupt labor markets more fundamentally: if machines can do workers’ jobs, wages would fall would disrupt labor markets more fundamentally: if machines can do workers’ jobs, wages would fall to machines’ user cost (Korinek and Juelfs, 2023). This would pose fundamental challenges for labor markets and income distribution (Korinek, 2023)."
Suggested mitigations
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Source
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Included resource
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.
