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The Human Edge in Testing AI Models by Pavan Teja
Testing AI applications demands a paradigm shift from traditional software testing. Unlike deterministic code, AI apps rely on data-driven models with unpredictable behaviors. Challenges include: • Validating accuracy and ensuring data quality • Detecting bias and interpreting black-box decisions Leveraging domain knowledge and exploratory skills, manual testers bring key assets to AI output testing. The path forward blends traditional QA rigor with AI expertise for reliable, fair, and robust AI applications.
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