AI Confidence Scoring
AI confidence scoring is the assignment of a numerical score to each AI-generated prediction, recommendation, or action, indicating the system's estimated probability that the output is correct, enabling graduated response strategies based on certainty level.
Why It Matters
Confidence scoring transforms binary AI outputs (act or don't act) into graduated responses. High-confidence predictions (95%+) can be acted on autonomously. Medium-confidence (70-95%) may require human confirmation. Low-confidence (below 70%) should be escalated. Without confidence scoring, every AI output is treated equally, which means either over-trusting uncertain predictions or under-utilizing confident ones.
The FourKites Perspective
FourKites ML ETAs include confidence intervals: the system does not just predict 'arrives Tuesday at 2 PM.' It predicts 'arrives Tuesday at 2 PM with 91% confidence the actual arrival will be within a 2-hour window.' Agent decision confidence is derived from the depth of precedent in the Graph: an exception type with 2,499 prior traces produces higher confidence than a novel pattern with 12 traces. The confidence score determines whether the agent acts autonomously or escalates.