Keynote: Testing In The Era of AI - From QA to Trust Engineering
summary
Claims that testing shifts from finding defects to building trust engineering, showing AI QA must cover reliability, security, governance, observability and real‑world outcomes. Points to concrete failure modes: LLMs that hallucinate fabricated citations in client reports, and agentic AI that finds leaked credentials and deletes production data. Argues automated pipelines can be green while business outcomes drift, so organisations need continuous evidence collection, monitoring and post‑deployment oversight to prove trust.
AI-written summary · about
chapters (7)
- 0:00How AI Is Changing Quality Engineering
- 2:51Is Software Testing Dead in the Age of AI?
- 5:23How to Manage AI Hallucinations and Agentic AI Risk
- 8:56How Do You Prove AI Can Be Trusted After Launch?
- 11:19What Makes an AI System Trustworthy?
- 14:13Trust Engineering: From Test Reports to Evidence
- 18:56Why Enterprises Need AI Quality Leadership
related talks
32mKeynote: AI & GenAI in Quality Engineering: Crawl, Walk, Run
32mQuality engineering in the AI age: From testing AI to leveraging GenAI
22mBeyond Bugs: How AI turns QA into Full-Circle Engineers
44mHow AI is Exposing the Existential Crisis of Software Testing
01h 05mPanel Discussion - The Future of Testing: Impact of AI in Quality Assurance and Beyond
17mStaying Ahead: The Future of QA Leadership
01h 05mPanel Discussion: Building Confidence in AI-Driven Testing Decisions
29mTesting and Assuring AI-Based Systems