SHOULD YOU TRUST THAT AI RECOMMENDATION? HERE'S HOW TO KNOW

SHOULD YOU TRUST THAT AI RECOMMENDATION? HERE'S HOW TO KNOW.
What every leader needs to know about AI risk, accountability, and decision-making

3 Nov 2026, 7:15pm - 9:15pm
Robertson Quay, Singapore (Venue details to be furnished upon successful registration)

AI is already shaping decisions that matter.

Who gets shortlisted for a job? Which patient is flagged as high risk? Which athlete is told to reduce training before an injury happens? Across industries, algorithms are increasingly influencing who gets an opportunity, who receives support, and what action is taken next.

But here is the question leaders cannot afford to ignore: when AI makes a prediction, should you act on it?A model can be highly accurate and still be wrong in the moments that matter most. It can work well on average while failing certain groups. It can identify risk without telling you what intervention will actually help. And when a recommendation leads to harm, delay, or a missed opportunity, accountability does not sit with the algorithm. It sits with people.

This is no longer just a technical conversation. In September 2026, Anthropic CEO Dario Amodei publicly called on the AI industry to slow down, warning that without stronger safeguards, the consequences could be serious within the next decade. Sam Altman and Elon Musk, two of the most influential and often fiercely opposed voices in AI, agreed. When the people building these systems are warning the world to be more careful, every organisation needs to ask: what are we trusting AI to decide, and what is the cost if it gets it wrong?

In this timely masterclass, Dae-Jin Lee, statistician and Assistant Professor at IE University, will unpack what it really takes to use AI responsibly when the stakes are high. Drawing on his work in sport and healthcare, he will show how predictive models can support real decisions, from assessing injury risk to anticipating a patient’s likely health journey, while revealing the assumptions and uncertainty behind every recommendation.

The conversation goes far beyond sport and healthcare. A model may perform well overall, but does it work equally well for everyone? It may succeed in one environment, but can it be trusted in another? It may identify someone as high risk, but does it actually tell us what action will help?This session will explore what meaningful human oversight looks like, why models need to be tested beyond their initial development, and who should be accountable when an AI-supported recommendation goes wrong.

You will leave with:

• A practical way to assess when an AI recommendation can be trusted
• Critical questions to ask before acting on a model’s prediction
• A clearer view of bias, uncertainty, accountability, and human oversight
• More confidence in knowing when human judgement needs to lead
• Walk in relying on AI's word. Walk out knowing exactly when to question it.

No technical background required. Just the willingness to ask better questions.

Important note: A successful registration on Eventbrite does not guarantee a slot. An IE representative will reach out to confirm your place, so please ensure you provide the correct email address and phone number when registering. As slots are limited, we encourage you to register early.