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Labor exploitation

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

Li et al. (2025)

Sub-category

Risk Domain

6.2Increased inequality and decline in employment quality

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

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

Entity: Who or what caused the harm

Human

Due to a decision or action made by humans

AI system

Due to a decision or action made by an AI system

Other

Due to some other reason or is ambiguous

Intent: Whether the harm was intentional or accidental

Intentional

Due to an expected outcome from pursuing a goal

Unintentional

Due to an unexpected outcome from pursuing a goal

Other

Without clearly specifying the intentionality

Timing: Whether the risk is pre- or post-deployment

Pre-deployment

Occurring before the AI is deployed

Post-deployment

Occurring after the AI model has been trained and deployed

Other

Without a clearly specified time of occurrence

Other risks from Li et al. (2025) (40)

Autonomy

5.2 Loss of human agency and autonomy

OtherOtherOther

Autonomy > Impersonation / identity theft

4.3 Fraud, scams, and targeted manipulation

HumanIntentionalPost-deployment

Misinformation Harms

3.1 False or misleading information

AI systemOtherPost-deployment

Representation and Toxicity

1.0 Discrimination & Toxicity

AI systemUnintentionalPost-deployment

IP / copyright / personality / rights loss

4.3 Fraud, scams, and targeted manipulation

HumanIntentionalPost-deployment

Autonomy / agency loss

5.2 Loss of human agency and autonomy

OtherOtherOther

View all 40 risks from this paper →