The Quality & Validation tab provides visibility into the known data quality issues, validation efforts, and bias-related considerations tied to a dataset. This section helps teams ensure that the dataset meets quality expectations and is suitable for use in AI systems, particularly in regulated or high-risk environments.
Purpose
This tab is designed to capture and surface key quality signals for datasets, including:
Documentation of known issues
Summary of validation activities
Status of bias and fairness evaluation
Maintaining quality and validation information for datasets helps support the integrity of AI systems that rely on them and provides evidence of due diligence for review or audit.
Key Sections
Known Data Quality Issues
Displays a summary of any documented data quality concerns. If no issues have been recorded, the tab will state that explicitly.Validation Summary
Shows any validation steps or outcomes previously captured for this dataset. This may include testing for consistency, completeness, labeling accuracy, or lineage validation.Bias & Fairness Assessment
Indicates whether a bias and fairness assessment has been completed. If not yet done, it will show as Not Completed.
Notes
This page is informational. It summarizes key indicators but does not provide editing or assessment functionality directly.
Use this tab to quickly verify whether a dataset has unresolved quality concerns or lacks validation.
If you need to run a bias and fairness assessment or log validation details, refer to the appropriate assessment tools available elsewhere in the dataset record or in the risk management module.
Proper documentation in this tab helps ensure data readiness and supports downstream governance efforts when the dataset is used in an AI system.