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MIT AI Risk Initiative
BackDiscrimination
Loss of privacy
Bias

Discrimination

A framework for ethical Ai at the United Nations

Hogenhout (2021)

SourceDOI
Category
Risk Domain
1Discrimination & Toxicity
1.1Unfair discrimination and misrepresentation

Unequal treatment of individuals or groups by AI, often based on race, gender, or other sensitive characteristics, resulting in unfair outcomes and unfair representation of those groups.

"When AI is not carefully designed, it can discriminate against certain groups."(p. 7)

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

Other risks from Hogenhout (2021) (12)

Incompetence

7.3 Lack of capability or robustness
AI systemUnintentionalPost-deployment

Loss of privacy

2.1 Compromise of privacy by leaking or correctly inferring sensitive information
HumanIntentionalPost-deployment

Bias

1.1 Unfair discrimination and misrepresentation
AI systemUnintentionalPre-deployment

Erosion of Society

3.2 Pollution of information ecosystem and loss of consensus reality
AI systemUnintentionalPost-deployment

Lack of transparency

7.4 Lack of transparency or interpretability
AI systemUnintentionalOther

Deception

4.3 Fraud, scams, and targeted manipulation
AI systemOtherPost-deployment
View all 12 risks from this paper →