Transparency in AI: Show your work
AI fosters speed. Transparency fosters confidence.
AI for QA testing is suddenly everywhere. But here's the uncomfortable truth: AI in QA only works if you can trust it.
Download our comprehensive, practical guide to transparent AI in QA. Get the playbook for evaluating AI tools the right way, before you trust them with your product.
Knowing you need AI transparency is the easy part.
Knowing how to evaluate for AI transparency is much harder.
What you'll learn
Why “AI-powered” doesn’t mean trustworthy in QA—and how transparency became the real differentiator.
How opaque AI creates false confidence in test results (and the subtle ways it hides real risk).
What “show your work” actually means for AI in QA, beyond marketing claims and demos.
How self-healing tests can quietly drift away from critical user flows and how to prevent this.
Where probabilistic AI helps QA teams move faster—and where determinism still matters most.
The five questions that cut through AI hype and reveal whether a QA tool earns real trust.
Key insight
Opaque AI isn’t just inconvenient.
It’s actively dangerous in AI for QA testing, because it can hide failing tests, inconsistent behavior, and broken selectors.
QA deserves clear signals, not hidden decisions.
Download the guideIt’s actively dangerous in AI for QA testing, because it can hide failing tests, inconsistent behavior, and broken selectors.
QA deserves clear signals, not hidden decisions.