Subset: AiRisksSubset

Entities from the DPV ai/modules/risks submodule.

Identifier and Mapping Information

Schema Source

from schema: https://w3id.org/lmodel/dpv/ai

Classes in subset

Class	Description
AdversarialAttack	Inputs designed to cause the model to make a mistake
AIBias	Bias associated with development, use, or other activities involving an
AiDataAggregationBias	Bias that occurs from aggregating data covering different groups of
AiDataBias	Bias that occurs due to unaddressed data properties that lead to AI
AiDataRisk	Risk associated with data used or produced or otherwise involved in the
AiInformativenessBias	Bias that occurs or some groups, the mapping between inputs present in
AiRiskConcept	Risk concepts such as risk sources, risks, consequences, and impacts
AiSecurityAttack	Risks or issues associated with security attacks related to AI
AISystemRisk	Risks associated with AI Systems
AlgorithmSelectionBias	Bias that occurs from the selection of machine learning algorithms built
AutomationBias	Bias that occurs due to propensity for humans to favour suggestions from
DataLabellingProcessBias	Bias that occurs due to the labelling process itself introducing
DataPoisoning	Attack trying to manipulate the training dataset
DistributedTrainingBias	Bias that occurs due to distributed machine having different sources of
EngineeringDecisionBias	Bias that occurs due to machine learning model architectures -
ExplainabilityRisk	Risk that an AI's decisions or behaviors cannot be adequately
FeatureEngineeringBias	Bias that occurs from steps such as encoding, data type conversion,
HyperparameterTuningBias	Bias that occurs from hyperparameters defining how the model is
InputDataBias	Concept representing input data containing or potentially containing
InputDataInaccurate	Concept representing input data being inaccurate
InputDataInappropriate	Concept representing input data being inappropriate
InputDataIncomplete	Concept representing input data being incomplete
InputDataInconsistent	Concept representing input data being inconsistent
InputDataMisclassified	Concept representing input data being misclassified
InputDataMisinterpretation	Concept representing input data being misinterpreted
InputDataNoise	Concept representing input data being noisy
InputDataOutdated	Concept representing input data being outdated
InputDataRisk	Risks and risk concepts related to input data
InputDataSelectionError	Concept representing an error in input data selection
InputDataSparse	Concept representing input data being sparse
InputDataUnrepresentative	Concept representing input data being unrepresentative
InputDataUnstructured	Concept representing input data being unstructured
InputDataUnverified	Concept representing input data being unverified
MissingFeaturesBias	Bias that occurs when features are missing from individual training
ModelBias	Bias that occurs when ML uses functions like a maximum likelihood
ModelEvasion	An input, which seems normal for a human but is wrongly classified by ML
ModelExpressivenessBias	Bias that occurs from the number and nature of parameters in a model as
ModelInteractionBias	Bias that occurs from the structure of a model to create biased
ModelInversion	A type of attack to AI models, in which the access to a model is abused
ModelRisk	Risks associated with AI Models
NonRepresentativeSamplingBias	Bias that occurs if a dataset is not representative of the intended
TestingDataBias	Concept representing testing data containing or potentially containing
TestingDataInaccurate	Concept representing testing data being inaccurate
TestingDataInappropriate	Concept representing testing data being inappropriate
TestingDataIncomplete	Concept representing testing data being incomplete
TestingDataInconsistent	Concept representing testing data being inconsistent
TestingDataMisclassified	Concept representing testing data being misclassified
TestingDataMisinterpretation	Concept representing testing data being misinterpreted
TestingDataNoise	Concept representing testing data being noisy
TestingDataOutdated	Concept representing testing data being outdated
TestingDataRisk	Risks and risk concepts related to testing data
TestingDataSelectionError	Concept representing an error in testing data selection
TestingDataSparse	Concept representing testing data being sparse
TestingDataUnrepresentative	Concept representing testing data being unrepresentative
TestingDataUnstructured	Concept representing testing data being unstructured
TestingDataUnverified	Concept representing testing data being unverified
TransparencyRisk	Risk that an AI's design, performance, outputs, or other characteristics
UserRisk	Risks associated with Users of AI Systems
ValidationDataBias	Concept representing validation data containing or potentially
ValidationDataInaccurate	Concept representing validation data being inaccurate
ValidationDataInappropriate	Concept representing validation data being inappropriate
ValidationDataIncomplete	Concept representing validation data being incomplete
ValidationDataInconsistent	Concept representing validation data being inconsistent
ValidationDataMisclassified	Concept representing validation data being misclassified
ValidationDataMisinterpretation	Concept representing validation data being misinterpreted
ValidationDataNoise	Concept representing validation data being noisy
ValidationDataOutdated	Concept representing validation data being outdated
ValidationDataRisk	Risks and risk concepts related to validation data
ValidationDataSelectionError	Concept representing an error in validation data selection
ValidationDataSparse	Concept representing validation data being sparse
ValidationDataUnrepresentative	Concept representing validation data being unrepresentative
ValidationDataUnstructured	Concept representing validation data being unstructured
ValidationDataUnverified	Concept representing validation data being unverified