What are the hidden risks when teams over-trust AI-generated test cases without human review?
What all errors AI testing Causes?
If you can’t trust and need to double or triple check. Might it be better to do it yourself?
How do organizations balance the pressure to adopt AI in testing with the risk of creating unsafe dependencies?
When we bring AI into software testing, what are the main things that can go wrong
Which industries are most likely to overcome the paradox first, and why?
. We do full regression testing using automation testing tools. How AI can be used/integrated with the automation testing tools?
What role will human-in-the-loop testing play in ensuring accountability for autonomous AI systems?
What happens if AI misinterprets application telemetry or logging data during testing?
Where should the line be drawn between automation efficiency and human ethical oversight?
What if there are certain use cases that get missed by AI while testing ?
How to make AI unlearn what need not to be learned ? Maybe in case it will create incorrect test cases.
What strategies help QA teams avoid treating AI tools as “black boxes” in critical decisions?
o what extent can AI-generated test cases introduce blind spots or missed scenarios due to the AI’s limits on understanding context or exercising human-like intuition?
If AI-driven testing is biased, aren’t we just embedding those biases deeper into our products under the name of ‘quality’?
If exhaustive testing is impossible, are we expecting the impossible from AI when we demand it be perfectly fair and unbiased?