What does Data Provenance track?

Data provenance focuses on tracing and documenting the history and origin of data within a dataset. This includes information about where the data came from, how it was collected, and any transformations it has undergone throughout its lifecycle. Understanding data provenance is essential for ensuring data integrity, as it provides insights into the sources and processes that produce the data.

By tracking data provenance, organizations can establish accountability and make informed decisions regarding data usage, particularly in contexts like AI governance where the origins of the data can significantly impact the outputs of AI systems. This transparency is critical for compliance and ethical considerations, as it allows stakeholders to assess the reliability and quality of the data being used.

The other options, while related to AI and data, do not align with the concept of data provenance. For example, tracking the accuracy of AI outputs focuses more on the results produced by algorithms rather than the data itself, and performance metrics of algorithms concern their operational effectiveness rather than the datasets used in training them. Thus, the correct understanding of data provenance is directly tied to the history and origin of records in a dataset.