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AI quality assurance should test what a system does when instructions are unclear, evidence conflicts or an action exceeds its authority, argues TestingXperts chief executive Manish Gupta in Forbes. He recommends evaluating outcomes in their business context, challenging systems beyond expected scenarios, and continuing to monitor them after deployment.
Why it mattersHis point is that a workflow can run correctly and still make a bad decision. For organisations giving AI more autonomy, testing needs to show not just that a system works, but when it should stop or ask for help.
Discuss: Should independent testing be required before businesses give AI systems more authority, or is internal assurance enough?
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