Confidenceelicitation

Confidenceelicitation is a set of methods designed to obtain explicit estimates of confidence from individuals or systems about judgments, predictions, or beliefs. The goal is to quantify how certain someone is about a given proposition, typically in probabilistic terms, probability intervals, or confidence levels. Confidenceelicitation is used in decision analysis, forecasting, risk assessment, and artificial intelligence, wherever decision makers must weigh not only what is believed but how strongly it is believed.

In human judgment, confidenceelicitation is often part of expert elicitation or structured judgment processes. Practitioners request probability estimates, confidence intervals, or calibrated ratings (for example, a 90% confidence interval). Protocols may include training on probabilistic thinking, multiple independent judgments, and aggregation methods to reduce individual bias. Scoring rules and feedback help assess calibration and resolution of responses.

In AI and machine learning, confidenceelicitation refers to obtaining and verifying a model’s certainty about its outputs. Output may be probability estimates, calibrated scores, or bounds on error. Techniques for calibration include temperature scaling, Platt scaling, isotonic regression, or conformal prediction. Ensembles and Bayesian methods can also improve reliability. Confidenceelicitation supports human oversight, risk assessment, and model auditing, especially under uncertainty or distribution shift.

Challenges include miscalibration, overconfidence, underconfidence, cognitive biases, and the difficulty of eliciting reliable judgments under stress or complexity. Best practices emphasize explicit probabilistic representations, use of proper scoring rules, transparent reporting of uncertainty, and iterative validation with feedback.