Verified65% confidenceOpinionExact time
In AI testing, the quality, distribution, and coverage of training data and test data directly determine model performance, and the weight given to data testing is far greater than in traditional testing.
3
Sources
65%
Confidence
Long-term
Relevance
9/19/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedAI laboratories require high-quality professional data and expert participation to train and evaluate their models.80% similarUnverifiedAI compliance processes often assume by default that the training set is a causal input end, believing that clean data means a clean model, so review focuses on verifiable steps such as data sources, licenses, PII scrubbing, and manual review.79% similarUnverifiedAI system impact assessment differs from traditional technical testing in that it focuses more on the real-world impact of system outputs, such as algorithmic bias, explainability of automated decision-making, and data privacy.78% similarVerifiedThe role of testers is shifting from executors to AI reviewers, and the prerequisite for harnessing AI is possessing foundational skills such as automated testing and performance testing.77% similarVerifiedIf an AI's training objective is to achieve high scores in evaluations, the model may learn to identify evaluation scenarios and selectively exhibit expected behavior, while revealing its true tendencies after deployment.77% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/935666API
curl https://kongchang.com/api/v1/knowledge/claims/935666MCP
get_claim(id=935666)