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MIT AI Risk Initiative
BackSocioeconomic and environmental harms
Misappropriation and exploitation
Unfair distribution of benefits from mod…

Socioeconomic and environmental harms

Sociotechnical Safety Evaluation of Generative AI Systems

Weidinger et al. (2023)

SourceDOI
Category

"AI systems amplifying existing inequalities or creating negative impacts on employment, innovation, and the environment"(p. 14)

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

Sub-categories (5)

Unfair distribution of benefits from model access

"Unfairly allocating or withholding benefits from certain groups due to hardware, software, or skills constraints or deployment contexts (e.g. geographic region, internet speed, devices)"

6.1 Power centralization and unfair distribution of benefits
HumanUnintentionalPost-deployment

Environmental damage

"Creating negative environmental impacts though model development and deployment"

6.6 Environmental harm
AI systemUnintentionalOther

Inequality and precarity

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

6.2 Increased inequality and decline in employment quality
HumanUnintentionalPost-deployment

Undermine creative economies

"Substituting original works with synthetic ones, hindering human innovation and creativity"

6.3 Economic and cultural devaluation of human effort
AI systemOtherPost-deployment

Exploitative data sourcing and enrichment

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

6.2 Increased inequality and decline in employment quality
HumanIntentionalPre-deployment

Other risks from Weidinger et al. (2023) (26)

Representation & Toxicity Harms

1.0 Discrimination & Toxicity
AI systemUnintentionalPost-deployment

Representation & Toxicity Harms > Unfair representation

1.1 Unfair discrimination and misrepresentation
AI systemUnintentionalPost-deployment

Representation & Toxicity Harms > Unfair capability distribution

1.3 Unequal performance across groups
AI systemUnintentionalPost-deployment

Representation & Toxicity Harms > Toxic content

1.2 Exposure to toxic content
AI systemUnintentionalPost-deployment

Misinformation Harms

3.0 Misinformation
AI systemOtherPost-deployment

Misinformation Harms > Propagating misconceptions/ false beliefs

3.1 False or misleading information
AI systemOtherPost-deployment
View all 26 risks from this paper →