Dataset Details – Quality & Validation

Overview 

The Dataset Details – Quality & Validation section provides visibility into data quality characteristics and validation processes associated with a dataset. 

It allows users to document information that helps assess whether the dataset is suitable for its intended use within AI systems. 

Maintaining data quality information supports transparency of dataset reliability. 

Purpose 

The Quality & Validation section helps organizations record information related to the condition and usability of datasets used in AI systems. 

Documenting quality and validation information supports governance processes by providing context on how data is reviewed, verified, and maintained. 

Maintaining this information helps ensure datasets are appropriate for their intended purpose. 

Key features 

data quality information 

Allows users to document characteristics related to dataset quality. 

This may include information describing completeness, consistency, or relevance of data. 

Maintaining quality information helps provide context on dataset usability. 

validation documentation 

Supports recording of validation activities performed on the dataset. 

Validation information helps indicate whether the dataset has been reviewed or assessed for its intended use. 

visibility of dataset condition 

Provides insight into the current condition of the dataset based on recorded quality and validation information. 

This helps users understand whether the dataset meets requirements for AI system usage. 

support for governance workflows 

Quality and validation information may support governance activities such as: 

Maintaining documentation supports transparency of dataset reliability. 

How to use 

access the quality & validation section 

Navigate to Datasets Dashboard and open a dataset record. 

Select Dataset Details – Quality & Validation. 

record quality information 

Enter relevant information describing dataset quality characteristics. 

Ensure information accurately reflects dataset condition. 

document validation activities 

Record validation processes performed on the dataset where applicable. 

Update validation information as required. 

review dataset quality details 

Review recorded information to ensure completeness and accuracy. 

Notes 

• Maintaining dataset quality and validation information helps support transparency of data used within AI systems. 

• Accurate documentation helps ensure datasets are appropriate for their intended use. 

• Quality and validation records contribute to governance oversight and traceability across the AI lifecycle.