Performance

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

ID	Sub-ID	Name	Description
P0100		Data issues	Problems arising due to faults in the data pipeline
	P0101	Data drift	Input feature distribution has drifted
	P0102	Concept drift	Output feature/label distribution has drifted
	P0103	Data entanglement	Cases of spurious correlation and proxy features
	P0104	Data quality issues	Missing or low-quality features in data
	P0105	Feedback loops	Unaccounted for effects of an AI affecting future data collection
P0200		Model issues	Ability for the AI to perform as intended
	P0201	Resilience/stability	Ability for outputs to not be affected by small change in inputs
	P0202	OOD generalization	Test performance doesn’t deteriorate on unseen data in training
	P0203	Scaling	Training and inference can scale to high data volumes
	P0204	Accuracy	Model performance accurately reflects realistic expectations
P0300		Privacy	Protect leakage of user information as required by rules and regulations
	P0301	Anonymization	Protects through anonymizing user identity
	P0302	Randomization	Protects by injecting noise in data, eg. differential privacy
	P0303	Encryption	Protects through encrypting data accessed
P0400		Safety	Minimizing maximum downstream harms
	P0401	Psychological Safety	Safety from unwanted digital content, e.g. NSFW
	P0402	Physical safety	Safety from physical actions driven by a AI system
	P0403	Socioeconomic safety	Safety from socioeconomic harms, e.g. harms to job prospects or social status
	P0404	Environmental safety	Safety from environmental harms driven by AI systems