Confident machines: Why AI's trust problem starts before the model ever runs

About this session

Knowledge is compounding faster than anyone can keep up with — in medicine, research output is doubling every 73 days; in almost every other field, the story is the same: more papers, more reports, more data than any person or team can read. People are drowning in information, yet starved for valuable insights they can trust.Every model builds or breaks trust at three layers: what it knows, how it reasons, and how it's deployed. Right now, the entire industry is obsessing over tuning guardrails and alignment, while the foundation underneath goes almost completely unchecked. The result: AI systems that sound certain while citing Reddit threads as gospel, hallucinating peer review out of thin air, and manufacturing false confidence at exactly the moments people need real answers. And the stakes are rising fast. We’re no longer just prompting a chatbot, we're deploying agents that search, retrieve, reason, and act in multi-step chains. If the knowledge layer is flawed, an agent doesn't make one mistake; it makes a decision, then builds more decisions on top of it, each one inheriting and compounding whatever it got wrong at the start. And the cost compounds too: now the mistake isn't caught early, it's caught downstream, where it's far more expensive to unwind. This session breaks down three layers of trust, and makes the case that the one nobody's watching is the one that decides everything else. This talk flips the responsible AI conversation upside down — from outputs to foundations — showing exactly what breaks when the knowledge layer is rotten, what happens when agentic systems build on it, and what becomes possible when it's solid.