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Back to the Basics

25-minute Talk

Stop reinventing testing for AI. The risks and techniques may change, but the core does not: understand what can go wrong, test what matters, and inform decisions.

Virtual Pass session

Timetable

10:45 a.m. – 11:30 a.m. Thursday 19th

Room

Room E1 - Track 4: Talks

Artificial Intelligence (AI) Quality Coaching Test Management

Audience

For testers, quality engineers, test leads, developers, and product leaders working with AI.

Key-Learnings

  • AI testing is not a new discipline; it is risk-based testing applied to different failure modes and uncertainties.
  • New AI quality characteristics add to—not replace—established concerns such as security, usability, and reliability.
  • Choose perspectives and techniques based on relevant risks—not on hype, tools, or generic AI testing checklists.

Testing in the Age of AI

AI has changed the conversation about testing more than it has changed testing itself. The industry talks about AI testing as if testers had never dealt with uncertainty before.

The systems may be new, but the core of testing is not. We still analyse risks, manage uncertainty, ask what could go wrong, and design tests that help people make better decisions. AI changes the failure modes, shifts priorities, and adds concerns such as bias, hallucinations, drift, explainability, non-deterministic behaviour, and ethical use. But it does not make security, usability, reliability, accessibility, or performance disappear.

In this talk, we will strip away the hype and return to the fundamentals. We will examine AI-enabled systems through the lens of risk-based testing: where established approaches still work, where our perspective must shift, and where new techniques add value.

The message is simple and clear: treat AI as a new risk landscape, not as an excuse to forget how good testing works.

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