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Environmental

The Risks of Machine Learning Systems

Tan, Taeihagh & Baxter (2022)

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.

The risk of harm to the natural environment posed by the ML system.(p. 15)

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 Second-Order Risks

Other risks from Tan, Taeihagh & Baxter (2022) (17)

First-Order Risks

7.0 AI System Safety, Failures & Limitations
OtherOtherOther

First-Order Risks > Application

7.0 AI System Safety, Failures & Limitations
HumanIntentionalPost-deployment

First-Order Risks > Misapplication

7.3 Lack of capability or robustness
HumanIntentionalPost-deployment

First-Order Risks > Algorithm

7.3 Lack of capability or robustness
AI systemUnintentionalPre-deployment

First-Order Risks > Training & validation data

7.0 AI System Safety, Failures & Limitations
HumanOtherPre-deployment

First-Order Risks > Robustness

7.3 Lack of capability or robustness
AI systemUnintentionalPost-deployment
View all 17 risks from this paper →