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Environmental cost (energy consumption)
Artificial general intelligence (existen…

Environmental cost (water consumption)

Regulating under Uncertainty: Governance Options for Generative AI

G'sell (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.

"Data centers use water for cooling to prevent servers from overheating. The water consumption associated with AI training and inference processes can be substantial, impacting local water resources."(p. 112)

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, economical, and societal challenges

Other risks from G'sell (2024) (33)

Technical and operational risks

7.3 Lack of capability or robustness
AI systemUnintentionalOther

Technical and operational risks > Technical vulnerabilities (Robustness - unexpected behaviour)

7.3 Lack of capability or robustness
AI systemOtherPost-deployment

Technical and operational risks > Technical vulnerabilities (Robustness - vulnerability to jailbreaking

2.2 AI system security vulnerabilities and attacks
HumanIntentionalPost-deployment

Technical and operational risks > Technical vulnerabilities (The risk of misalignment)

7.1 AI pursuing its own goals in conflict with human goals or values
AI systemOtherPost-deployment

Technical and operational risks > Factually incorrect content (inaccuracies and fabricated sources)

3.1 False or misleading information
AI systemUnintentionalPost-deployment

Technical and operational risks > Opacity (the black box problem)

7.4 Lack of transparency or interpretability
OtherUnintentionalOther
View all 33 risks from this paper →