Better Wrong Than Certain: How AI Learns to Know When It Doesn’t Know
A credit model approves the familiar applicant. A diagnostic model reads the common scan. A pricing model values the house in a neighbourhood it has seen a thousand times before. Everyone relaxes. The model is “confident”. Then a strange case arrives. The applicant has an unusual income pattern. The scan comes from an underrepresented patient group. The house sits outside the areas covered by historic transactions. The model still produces an answer, because that is what models are trained to do. Press button, receive number. Very efficient. Occasionally ridiculous. ...