MIT AI Risk Repository · Risk Sub-Category · 61.02.25

Exploitation in AI development

Category: Sources of systemic risks from general-purpose AI

Description

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

From A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025), as extracted by the MIT AI Risk Repository (CC BY 4.0).

Classification

Causal entity
Human
Intent
Other

Subdomain definition: 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.

Real-world incidents in this subdomain

Browse all incidents in this subdomain

How other frameworks describe this risk

  • Job loss/losses

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Societal destabilisation

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Societal inequality

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Labour exploitation

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Political instability

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Increased income disparity

    The Rise of Artificial Intelligence - Future Outlooks and Emerging Risks (Allianz2018)

  • Economic

    The Rise of Artificial Intelligence - Future Outlooks and Emerging Risks (Allianz2018)

  • Effects on the Workforce

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

Other entries from Uuk2025