BackService/benefit loss
Service/benefit loss
Risk Domain
Accuracy and effectiveness of AI decisions and actions are dependent on group membership, where decisions in AI system design and biased training data lead to unequal outcomes, reduced benefits, increased effort, and alienation of users.
degraded or total loss of benefits of using algorithmic systems with inequitable system performance based on identity(p. 731)
Entity— Who or what caused the harm
Intent— Whether the harm was intentional or accidental
Timing— Whether the risk is pre- or post-deployment
Part of Quality-of-Service Harms
Other risks from Shelby et al. (2023) (24)
Representational Harms
1.1 Unfair discrimination and misrepresentationOtherUnintentionalPost-deployment
Representational Harms > Stereotyping social groups
1.1 Unfair discrimination and misrepresentationAI systemUnintentionalPost-deployment
Representational Harms > Demeaning social groups
1.1 Unfair discrimination and misrepresentationAI systemUnintentionalPost-deployment
Representational Harms > Erasing social groups
1.3 Unequal performance across groupsHumanUnintentionalOther
Representational Harms > Alienating social groups
1.1 Unfair discrimination and misrepresentationAI systemUnintentionalPost-deployment
Representational Harms > Denying people the opportunity to self-identify
1.1 Unfair discrimination and misrepresentationAI systemUnintentionalPost-deployment