Data Quality
The fitness of dataset(s) for an AI system’s intended purpose, judged by attributes like relevance, accuracy, representativeness, completeness and provenance.
Definition
In the context of AI and machine learning, data quality refers to the degree to which data characteristics satisfy the stated and implied needs for a particular application or use case. It encompasses various dimensions such as accuracy, completeness, consistency, timeliness, and relevance, which are critical for reliable AI model performance.
Related Terms
Training Data
Data used to develop and train AI models to perform their intended functions....
Data Governance
Policies, roles and processes that ensure data used across the AI lifecycle is fit for purpose, traceable, secure and managed in line with legal and operational requirements....
Data Provenance
Documentation of data origin, collection, and transformations for AI systems....
Bias Mitigation
Measures to identify and reduce discriminatory outcomes in AI systems....