false positive

Terms from Artificial Intelligence: humans at the heart of algorithms

Page numbers are for draft copy at present; they will be replaced with correct numbers when final book is formatted. Chapter numbers are correct and will not change now.

A false positive is when a classification or decision system gives a positive result when it should really be negative; for example if a medical diagnosis system says someone has a disease, when they actually are healthy.

This is in contrast to with a true positive, a false negative or a true negative.

Used in Chap. 9: pages 130, 131, 140; Chap. 12: page 195; Chap. 18: pages 308, 310; Chap. 19: page 324; Chap. 20: page 345