MIT AI Risk Repository

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55 risk entries extracted from 74 frameworks, coded by domain, subdomain, causal entity, intent and timing. Filter, then export the current selection with its licence and citation attached.

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  1. 72.04.01 · Risk Sub-Category

    Systemic Risks

    Labor Market Disruption and Economic Displacement:

    "Rapid automation enabled by general-purpose AI could trigger widespread unemployment across knowledge work sectors, creating skill mismatches faster than retraining programs can address. Unlike previous technological transitions, AI’s broad capabilities may simultaneously affect multiple industries, potentially overwhelming social safety nets and creating systemic economic instability, particularly in regions heavily dependent on jobs susceptible to AI automation."

    From Frontier AI Risk Management Framework (v1.0) (Tse2025)

  2. 72.04.04 · Risk Sub-Category

    Systemic Risks

    Social Cohesion and Equity Disruption:

    "Systemic deployment of biased AI systems could exacerbate existing social discrimination and prejudice at unprecedented scales, while unequal access to advanced AI capabilities may widen socioeconomic disparities and create new forms of social stratification that challenge traditional social order."

    From Frontier AI Risk Management Framework (v1.0) (Tse2025)

  3. 73.05.01 · Risk Sub-Category

    Socioeconomic Impacts of LLM May Be Highly Disruptive

    Effects on the Workforce

    "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, meani

    From Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  4. 73.05.02 · Risk Sub-Category

    Socioeconomic Impacts of LLM May Be Highly Disruptive

    Effects on Inequality

    "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 LLM

    From Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  5. 73.05.03 · Risk Sub-Category

    Socioeconomic Impacts of LLM May Be Highly Disruptive

    Global Economic Development

    "Many of the themes and challenges that we discussed above come together when analyzing the socioeconomic effects on developing countries. The workforce of developing countries may suffer from a retrenchment of outsourcing as many simple cognitive tasks that used to be performed in developing countries — for example, in call centers –— can be automated with LLMs. This may adversely affect the economies of the poor countries (Georgieva, 2024)."

    From Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

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