Calibrated Confidence: Why Knowing When the Model Is Wrong Matters More Than When It's Right

Overview:

A working session on model calibration and uncertainty in deployed AI systems. We cover why frontier-model confidence is often miscalibrated, the practical techniques available to surface real uncertainty, and the system design patterns that make AI systems fail loudly instead of quietly. Examples are drawn from finance, healthcare, legal, and operations deployments where confident wrong answers carry real cost.

Why you should Attend:

Your AI demo wowed leadership because the answers sounded right. Now it is live, and one wrong answer just landed in front of a customer, a regulator, or an auditor. The model cannot tell you which answer it is least sure about, unless you have built the system to ask the question.

Areas Covered in the Session:

• Confidence versus calibration: definitions worth getting right
• Why "the model said so" is the wrong stopping point
• Where frontier models are systematically over-confident
• Eliciting uncertainty: prompt techniques, self-consistency, and ensembles
• Verifier patterns: cross-model checks and tool-grounded validation
• Designing for fail-loud, not fail-silent
• Human review thresholds: when to escalate, when to let it run
• Logging what you do not know you do not know
• Case patterns from finance, healthcare, legal, and operations
• Building calibration into evaluation harnesses from day one

Who Will Benefit:

• AI Engineers
• ML Engineers
• AI Product Managers
• Model Risk Officers
• Model Validation Leads
• Compliance Officers
• Risk Officers
• Engineering Managers
• Heads of AI
• CTOs
• Chief Data Officers
• Chief AI Officers
• Data Science Leaders