MIT AI Risk Repository · domain 6: Socioeconomic & Environmental

6.2 Increased inequality and decline in employment quality

Widespread use of AI increasing social and economic inequalities, such as by automating jobs, reducing the quality of employment, or producing exploitative dependencies between workers and their employers.

Risk entries
55
Frameworks citing it
12
Recorded incidents
6
Incidents since 2020
3
Causal entity (risk entries)
Causal entity (risk entries) 25 0 Human: 25 Human 25 AI: 18 AI 18 Other: 11 Other 11 Not coded: 1 Not coded 1
Causal entity (risk entries)
LabelValue
Human25
AI18
Other11
Not coded1
Intent (risk entries)
Intent (risk entries) 23 0 Other: 23 Other 23 Intentional: 19 Intentional 19 Unintentional: 12 Unintentional 12 Not coded: 1 Not coded 1
Intent (risk entries)
LabelValue
Other23
Intentional19
Unintentional12
Not coded1
Timing (risk entries)
Timing (risk entries) 40 0 Post-deployment: 40 Post-deployment 40 Other: 8 Other 8 Pre-deployment: 6 Pre-deployment 6 Not coded: 1 Not coded 1
Timing (risk entries)
LabelValue
Post-deployment40
Other8
Pre-deployment6
Not coded1
Recorded incidents per yearIncident date; current year partial
Recorded incidents per year 1 0 2014: 1 2014 1 2015: 1 2015 1 2016: 1 2016 1 2021: 1 2021 1 2023: 1 2023 1 2024: 1 2024 1
Recorded incidents per year
LabelValue
20141
20151
20161
20211
20231
20241
Entries by levelRisk categories, subcategories and additional evidence coded to this subdomain
Entries by level 48 0 Risk Category: 7 Risk Category 7 Risk Sub-Category: 48 Risk Sub-Category 48
Entries by level
LabelValue
Risk Category7
Risk Sub-Category48
  • Under-recognized work

    "Without training data, ML cannot take place. Much of this data comes from paid clickwork (also called “platform work” [170] or “microwork” [558]), unpaid crowdsourcing, and unpaid user behavior captu...

    Ten Hard Problems in Artificial Intelligence We Must Get Right (Leech2024 ) · Other · Other · Pre-deployment

  • Job loss

    "Replacement/displacement of human jobs by a technology system or set of systems, leading to increased unemployment, inequality, reduced consumer spending and social friction"

    A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025) · Other · Other · Other

  • Labor exploitation

    "Use/misuse of labour to help train, develop, manage or optimise a technology system or set of systems, including under-paid and/or offshore"

    A Closer Look at the Existing Risks of Generative AI: Mapping the Who, What, and How of Real-World Incidents (Li2025) · Human · Intentional · Pre-deployment

  • Inequality of wealth

    "Because a single human actor controlling an artificially intelligent agent will be able to harness greater power than a single human actor, this may create inequalities of wealth"

    Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review (Meek2016) · Human · Intentional · Post-deployment

  • Direct competition with humans

    "One or more artificial agent(s) could have the capacity to directly outcompete humans, for example through capacity to perform work faster, better adaptation to change, vaster knowledge base to draw...

    Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review (Meek2016) · AI · Intentional · Post-deployment

  • Competing for jobs

    "AI agents may compete against humans for jobs, though history shows that when a technology replaces a human job, it creates new jobs that need more skills."

    Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review (Meek2016) · AI · Other · Post-deployment

  • Labor market

    "The labor market can face challenges from generative AI. As mentioned earlier, generative AI could be applied in a wide range of applications in many industries, such as education, healthcare, and ad...

    Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration (Nah2023) · Human · Intentional · Post-deployment

  • Disruption of Industries

    "Industries that require less creativity, critical thinking, and personal or affective interaction, such as translation, proofreading, responding to straightforward inquiries, and data processing and...

    Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration (Nah2023) · Human · Intentional · Post-deployment

  • Income inequality and monopolies

    "Generative AI can create not only income inequality at the societal level but also monopolies at the market level. Individuals who are engaged in low-skilled work may be replaced by generative AI, ca...

    Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration (Nah2023) · Human · Intentional · Post-deployment

  • Usurpation of jobs by automation

    "Eliminated jobs in various types of companies."

    Social Impacts of Artificial Intelligence and Mitigation Recommendations: An Exploratory Study (Paes2023) · Human · Intentional · Post-deployment

  • Labour Displacement

    "While virtual AI applications will likely displace certain types of human cognitive labor, EAI systems could significantly replace or displace physical human labor [90]. At a minimum, EAI will likely...

    Embodied AI: Emerging Risks and Opportunities for Policy Action (Perlo2025) · AI · Unintentional · Post-deployment

  • Socioeconomic Inequality

    "Along with displacing labor, EAI could significantly exacerbate wealth inequalities. Those who have access to or own EAI systems will be able to automate labor and perform many tasks significantly be...

    Embodied AI: Emerging Risks and Opportunities for Policy Action (Perlo2025) · AI · Unintentional · Other

  • Data and Content Moderation Labor

    "Two key ethical concerns in the use of crowdwork for generative AI systems are: crowdworkers are frequently subject to working conditions that are taxing and debilitative to both physical and mental...

    Evaluating the Social Impact of Generative AI Systems in Systems and Society (Solaiman2023) · Human · Intentional · Pre-deployment

  • Ethical Risks (Risks of exacerbating social discrimination and prejudice, and widening the intelligence divide)

    "AI can be used to collect and analyze human behaviors, social status, economic status, and individual personalities, labeling and categorizing groups of people to treat them discriminatingly, thus ca...

    AI Safety Governance Framework (TC2602024) · Human · Intentional · Post-deployment

  • 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 previ...

    Frontier AI Risk Management Framework (v1.0) (Tse2025) · AI · Unintentional · Post-deployment

  • 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 socioeconomi...

    Frontier AI Risk Management Framework (v1.0) (Tse2025) · Human · Unintentional · Post-deployment

  • Economy

    "Economic disruptions ranging from large impacts on the labor market to broader economic changes that could lead to exacerbated wealth inequality, instability in the financial system, labor exploitati...

    A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025) · Other · Other · Post-deployment

  • Ability to automate jobs

    "The ability to automate jobs by AI models and systems can lead to significant job displacement, economic disruption, and social inequality."

    A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025) · AI · Intentional · Post-deployment

  • Capabilities that enable substitution of humans

    "The progressive replacement of human roles by AI models and systems can lead to societal disruption."

    A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025) · AI · Intentional · Post-deployment

  • Exploitation in AI development

    "Outsourcing tasks like data labeling to low-income countries can perpetuate inequality."

    A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025) · Human · Other · Pre-deployment

  • Increasing inequality and negative effects on job quality

    "Advances in LMs, and the language technologies based on them, could lead to the automation of tasks that are currently done by paid human workers, such as responding to customer-service queries, tran...

    Ethical and social risks of harm from language models (Weidinger2021) · Human · Other · Post-deployment

  • Increasing inequality and negative effects on job quality

    "Advances in LMs and the language technologies based on them could lead to the automation of tasks that are currently done by paid human workers, such as responding to customer-service queries, with n...

    Taxonomy of Risks posed by Language Models (Weidinger2022) · Human · Other · Post-deployment

  • Inequality and precarity

    "Amplifying social and economic inequality, or precarious or low-quality work"

    Sociotechnical Safety Evaluation of Generative AI Systems (Weidinger2023) · Human · Unintentional · Post-deployment

  • Exploitative data sourcing and enrichment

    "Perpetuating exploitative labour practices to build AI systems (sourcing, user testing)"

    Sociotechnical Safety Evaluation of Generative AI Systems (Weidinger2023) · Human · Intentional · Pre-deployment

  • Workforce substitution and transformation

    "Frey and Osborne (2017) analyzed over 700 different jobs regarding their potential for replacement and automation, finding that 47 percent of the analyzed jobs are at risk of being completely substit...

    The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration (Wirtz2020) · Other · Intentional · Post-deployment