Why does a confidence score matter?#
A confidence score provides a measure of reliability, allowing your team to automatically accept high-confidence predictions while flagging low-confidence ones for human review, thereby managing risk and improving efficiency.
How is a confidence score calculated?#
When an AI model analyses data, it calculates the probability for each possible outcome based on the patterns it learned during training. For example, an AI processing customer support emails might calculate an 85% probability that an email is about a billing issue, a 10% chance it's a technical problem, and a 5% chance it's a sales inquiry.
The highest probability—in this case, 85%—is presented as the confidence score for its conclusion. This score is not a measure of absolute truth, but rather the model's internal assessment of how well the new data matches the examples it has seen before. A high score indicates a strong match, while a low score suggests the data is unusual or ambiguous and may require human attention.
How is a confidence score different from accuracy?#
A confidence score measures the model's certainty in a single prediction, while a metric like accuracy measures its overall performance across thousands of past predictions. An AI can have high overall accuracy but still produce a low confidence score on a specific, unusual case, correctly signaling the need for human oversight.