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MIT AI Risk Initiative
BackExcessive water consumption
Excessive landfill
Natural resource depletion

Excessive water consumption

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

Abercrombie et al. (2024)

SourceDOI
Sub-category
Risk Domain
6Socioeconomic & Environmental
6.6Environmental harm

The development and operation of AI systems causing environmental harm, such as through energy consumption of data centers, or material and carbon footprints associated with AI hardware.

"Excessive water consumption - Excessive use of water to cool data centres and for other purposes, leading to water restrictions or shortages for local communities or businesses."(p. 20)

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

Part of Environmental

Other risks from Abercrombie et al. (2024) (53)

Autonomy

5.2 Loss of human agency and autonomy
OtherOtherOther

Autonomy > Autonomy/agency loss

5.2 Loss of human agency and autonomy
OtherOtherOther

Autonomy > Impersonation/identity theft

4.3 Fraud, scams, and targeted manipulation
HumanIntentionalPost-deployment

Autonomy > IP/copyright loss

4.3 Fraud, scams, and targeted manipulation
HumanIntentionalOther

Autonomy > Personality rights loss

5.2 Loss of human agency and autonomy
OtherOtherOther

Financial and business

4.3 Fraud, scams, and targeted manipulation
HumanOtherPost-deployment
View all 53 risks from this paper →