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Risk profile
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"LLMs could potentially worsen socioeconomic inequalities (Capraro et al., 2023). Effects on inequal- ity are closely linked to the effects of LLMs on workers but ultimately depend on how the fruits of technological progress are distributed...First, if the role and compensation of capital rise and the role and compensation of labor decline in an LLM-powered economy, inequality may go up because work is the main source of income for the majority of people...Second, the large fixed cost of training cutting-edge LLMs and the network effects involved imply that the market for the most advanced LLMs tends towards a natural monopoly structure in which only one or a small number of players will be successful, a phenomenon that has been termed ‘algorithmic monoculture’ in the literature (Kleinberg and Raghavan, 2021; Bommasani et al., 2022). As a result, LLM developers may amass significant market power. This might result in reduced social welfare, and lead to LLM-providers extracting monopoly rents from their customers (Kleinberg and Raghavan, 2021; Jagadeesan et al., 2023)...Third, as LLMs are becoming more powerful, who has access and who hasn’t is becoming a more and more important question. For example, automated coding tools have been shown to produce significant productivity gains, e.g. > 50% in some cases (Peng et al., 2023). Individuals who don’t have access —– whether it is for financial reasons, for reasons of education, because of corporate or governmental policies, or for geopolitical reasons — might be at a growing disadvantage"
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.
