AI Governance Needs a Test Plan: Turning Principles into Evidence

About this event

Join our newsletter to stay informed about Innovate QA’s virtual and in-person events, opportunities, news, and offers. Subscribe to the Innovate QA Newsletter (https://preview.mailerlite.io/forms/1828078/166995591270762256/share) Speaker: Tanvi Mittal | LinkedIn (https://www.linkedin.com/in/ACoAAAP9nNQBwgh0BtUYvdtnTgXFPsLVmRyhcDY) Topic: AI Governance Needs a Test Plan: Turning Principles into Evidence Agenda: 🕛 4:00–4:05 PM PST — Welcome 🕛 4:05–4:55 PM PST — Tanvi Mittal | LinkedIn (https://www.linkedin.com/in/ACoAAAP9nNQBwgh0BtUYvdtnTgXFPsLVmRyhcDY) \- AI Governance Needs a Test Plan: Turning Principles into Evidence 🕛 4:55–5:00 PM PST — Q&A & Closing Summary : AI governance is increasingly shaped by frameworks such as the EU AI Act, the NIST AI Risk Management Framework, and ISO/IEC 42001. These frameworks define what AI systems need to demonstrate around safety, fairness, transparency, accountability, and risk management, but they often leave an important question unanswered: how do you actually prove it? In this session, Tanvi Mittal will connect AI governance principles to practical Quality Engineering and testing practices. The discussion will explore how requirements around risk management, robustness, human oversight, and continuous monitoring can be translated into concrete QA artifacts and evidence. Tanvi will introduce PROBE — Probabilistic Baseline, Red Team It, Observe in Production, Behavioral Contracts, and Evals Not Assertions — and show how each part can be applied to testing non-deterministic AI systems. The session will also explore how QA teams can create practical evidence for AI behavior, including distributions, adversarial test suites, production monitoring, behavioral contracts, and evaluation gates. Takeaways : What attendees will learn: 1. How to translate AI governance principles into practical QA and testing activities 2. How probabilistic baselines can help establish expected AI system behavior 3. How to use red-team testing to identify risks and weaknesses in AI systems 4. How production observation and continuous monitoring can provide evidence of AI behavior 5. How behavioral contracts can turn governance expectations into testable requirements 6. How evaluation gates can be used to assess non-deterministic AI systems beyond traditional assertions 7. How QA teams can provide evidence of AI system behavior to auditors and executives 🎙️ Speaker Opportunities at Our Meetups Have expertise in QA, leadership, or tech skills you'd love to share? We're always looking for engaging speakers for our monthly meetups. This is a fantastic platform to showcase your knowledge, connect with the community, and inspire others. If you're interested submit your topic here (https://docs.google.com/forms/d/e/1FAIpQLSfrTCU-5qxxEwWKydzFRclbgiCOEsNOA6_d2bjLBi6K6cCHgA/viewform?usp=publish-editor) — we’d love to hear from you!