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Security

The Risks of Machine Learning Systems

Tan, Taeihagh & Baxter (2022)

Sub-category

Risk Domain

2.2AI system security vulnerabilities and attacks

Vulnerabilities that can be exploited in AI systems, software development toolchains, and hardware, resulting in unauthorized access, data and privacy breaches, or system manipulation causing unsafe outputs or behavior.

This is the risk of loss or harm from intentional subversion or forced failure.(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

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 →