⚖️ Advanced Course, Question Two: Accurate enough for what, for whom, and at what cost when it fails? This is the one technical teams and compliance teams answer differently without realizing it. One reports accuracy, the other needs to know risk. The gap between them? That's where things go wrong. Because accuracy is a technical metric, while risk is a human judgment. A model can post a great score and still be the wrong tool for the job. The trick is to ask what an error actually means once it leaves the spreadsheet: 📌 A false positive in a fraud model: a real customer locked out of their own account. 📌 A false negative: the company misses the fraud it was built to catch. 📌 A performance gap across groups in a hiring tool: that's discrimination, not a rounding error. 📌 A "small" error rate in a healthcare workflow: someone's life, on the line. Same metric. Wildly different stakes. A tool that summarizes your meeting notes can afford to be wrong sometimes. A tool deciding who gets hired, treated, or approved cannot. And a system that's 95% accurate overall can still be wrong about the same group nearly every time. "Accurate" for the average user is cold comfort if you're the one it keeps missing. So don't stop at "how accurate is it." Ask: 🔹 What kind of error matters most here? 🔹 Who pays for it when it happens? 🔹 What failure rate would we actually accept if it were us? That last one is the whole game. The people who design these systems are almost never the people the errors land on, which is exactly how a number on a slide gets to look like success while someone, somewhere, keeps paying for the miss. 📋 Save this and bring it to work. Question Three drops next week: what we owe the people behind the data. Follow course creator and AJL Senior Advisor Rebekah Tweed (@_bekah_) for more insights on this topic.