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MIT AI Risk Initiative
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Organizational

The Risks of Machine Learning Systems

Tan, Taeihagh & Baxter (2022)

SourceDOI
Sub-category

The risk of financial and/or reputational damage to the organization building or using 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 →