In AI development, what is the difference between validation and verification?

• A. Verification checks data lineage accuracy.
• B. Validation checks if the model meets user needs and objectives.
• C. Validation checks correct implementation and compliance with specifications.
• D. Verification checks data labeling quality.

Answer: (B)

In AI development, it’s about two lenses: building the thing right versus building the right thing. Verification asks whether the system is constructed correctly—whether the model and its components, along with data pipelines, conform to the specifications and design requirements. Validation asks whether the system actually meets user needs and objectives in real-world use, ensuring the solution delivers the intended outcomes for stakeholders.

That’s why the statement about validation—checking if the model meets user needs and objectives—is the best description. The other points relate more to how the system is built or to data quality aspects (like data lineage or labeling) and fall under verification or ancillary quality checks rather than whether the product serves the right purpose for users.