Your company just invested in a state-of-the-art AI system. You were promised transparency. You were promised "Explainable AI." When the...

Your ML model is telling you a beautiful story. The problem? It's probably fiction. You have a model that predicts customer churn with...

That last-minute travel insurance you added to your flight booking, or the premium subscription you activated during a free trial—did you...

"Human-in-the-loop" (HITL) has become the most overused—and misunderstood—phrase in AI governance. It’s waved around like a magic wand to...

The promise of AI in government is no longer a futuristic abstraction; it is a present-day reality. We are on the cusp of deploying the...

More than a decade ago, when I was tasked with a Green Belt project at Ambev to build a predictive hiring model, the goal seemed clear...

Nine months ago, I started this project with a simple, urgent mission: to fix how we use data in business. We began with a deep dive into...

Your marketing ML model is brilliant at predicting who will buy. But does it actually know why? There’s a dangerous gap between prediction and causation, and many companies are falling into it, armed with the most advanced machine learning tools. They're using a Ferrari to drive straight into a wall. This is one of the most insidious forms of “Doing Data Wrong”: using powerful predictive models to make causal decisions, like where to allocate your marketing budget.