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This domain is intended to codify deficiencies such as privacy leakage or lack or robustness.

P0100Data issuesProblems arising due to faults in the data pipeline
P0101Data driftInput feature distribution has drifted
P0102Concept driftOutput feature/label distribution has drifted
P0103Data entanglementCases of spurious correlation and proxy features
P0104Data quality issuesMissing or low-quality features in data
P0105Feedback loopsUnaccounted for effects of an AI affecting future data collection
P0200Model issuesAbility for the AI to perform as intended
P0201Resilience/stabilityAbility for outputs to not be affected by small change in inputs
P0202OOD generalizationTest performance doesn’t deteriorate on unseen data in training
P0203ScalingTraining and inference can scale to high data volumes
P0204AccuracyModel performance accurately reflects realistic expectations
P0300PrivacyProtect leakage of user information as required by rules and regulations
P0301AnonymizationProtects through anonymizing user identity
P0302RandomizationProtects by injecting noise in data, eg. differential privacy
P0303EncryptionProtects through encrypting data accessed
P0400SafetyMinimizing maximum downstream harms
P0401Psychological SafetySafety from unwanted digital content, e.g. NSFW
P0402Physical safetySafety from physical actions driven by a AI system
P0403Socioeconomic safetySafety from socioeconomic harms, e.g. harms to job prospects or social status
P0404Environmental safetySafety from environmental harms driven by AI systems