Testing AI : five obstacles and seven workarounds
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AI testing presents unique challenges including obscurity, fragility, social intrusion, wishful claims, and hype-driven criticism resistance. Traditional test cases are insufficient for AI systems. Effective AI testing requires applying core testing skills like analysis, investigation, and critical thinking while understanding testing's essential missions. The approach focuses on actionable steps for ensuring reliability and robustness of AI-powered applications, practical strategies to navigate AI hype, and expert insights for analyzing AI and LLM-based products.
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