Confidence Thresholding

Description

Confidence thresholding uses a model score to decide whether a prediction should proceed automatically or receive different handling. For classification, a defined reject variant accepts the most likely class only when its estimated probability exceeds a chosen threshold and otherwise withholds the automatic decision. Thresholds can route withheld cases to specified review or fallback procedures, but those procedures require their own validation. Recording the score, threshold and resulting route makes the decision rule inspectable. The operating point trades automatic coverage against accepted errors, and an estimated score is not a guarantee that a decision is correct or safe.

Example Use Cases

Safety

Implementing tiered confidence thresholds in autonomous vehicle decision-making where high-confidence lane changes (>98%) execute automatically, medium-confidence decisions (85-98%) trigger additional sensor verification, and low-confidence situations (<85%) engage conservative defensive driving modes or request human takeover.

A diabetic eye-screening service has its image classifier pass the cases it is least sure about to an ophthalmologist. The service picks the referral share, for example the least certain 10 to 20 per cent of images, so that accuracy on the cases handled automatically meets its target.

General

A payment processor's fraud model approves transactions it scores as clearly legitimate, runs extra automated checks on middling scores, and sends the least certain ones to an analyst. The thresholds are set so that the analyst queue stays within the team's daily capacity.

Transparency

A council's tool that flags possible errors in benefit claims shows caseworkers whether each flag is above or below its confidence threshold. The council publishes the share of claims sent to manual review and the error rate among those handled automatically, so the public can see when the tool declines to decide.

Limitations

Many models produce poorly calibrated confidence scores that don't accurately reflect true prediction uncertainty, leading to overconfident predictions for incorrect outputs or underconfident scores for correct predictions.
Threshold selection requires careful calibration and domain expertise, as inappropriate thresholds can either overwhelm human reviewers with too many cases or miss genuinely uncertain decisions that need oversight.
High-confidence predictions may still be incorrect or harmful, particularly when models encounter adversarial inputs, out-of-distribution data, or systematic biases that the confidence mechanism doesn't detect.
Static thresholds may become inappropriate over time as model performance degrades, data distribution shifts occur, or operational contexts change, requiring ongoing monitoring and adjustment.
Withholding a prediction does not make the subsequent process safe. Review delays, failed routing and inadequate fallback capacity can undermine the intended safeguard.

Resources

Research Papers

On optimum recognition error and reject tradeoff

C. Chow•Jan 1, 1970

The performance of a pattern recognition system is characterized by its error and reject tradeoff. This paper describes an optimum rejection rule and presents a general relation between the error and reject probabilities and some simple properties of the tradeoff in the optimum recognition system. The error rate can be directly evaluated from the reject function. Some practical implications of the results are discussed. Examples in normal distributions and uniform distributions are given.

Software Packages

scikit-learn

scikit-learn: machine learning in Python. Contribute to scikit-learn/scikit-learn development by creating an account on GitHub.

Tutorials

Post-tuning the decision threshold for cost-sensitive learning — scikit-learn

Once a classifier is trained, the output of the predict method outputs class label predictions corresponding to a thresholding of either the decision_function or the predict_proba output. For a bin...

Documentations

Tuning the decision threshold for class prediction — scikit-learn user guide

Classification is best divided into two parts: the statistical problem of learning a model to predict, ideally, class probabilities;, the decision problem to take concrete action based on those pro...

Related Techniques

Name	Description	Assurance Goals
Human-in-the-Loop Safeguards	Human-in-the-loop safeguards hold an AI system's output at a checkpoint until a trained person has reviewed it. Designers set the rules that send a case to review, such as low model confidence (the system's own estimate of how likely it is to be right), a high-stakes outcome or a request to appeal. Some schemes also review a random sample of routine cases to audit the rest. The reviewer approves, changes or rejects the output, and the record of each review shows how often people overrode the system. Review protects only where reviewers keep using independent judgement, so the design also has to guard against over-trust and fatigue.	General Safety Transparency
Out-of-Domain Detection	Out-of-domain detection checks whether a user's query falls inside the topics and tasks a conversational AI system is meant to handle. Scope is defined by examples, such as an assistant's supported intents (the actions it can recognise), and a detector then scores how closely each new query resembles them. Typical detectors set a minimum confidence on an intent classifier, add an explicit out-of-scope class trained on examples of unsupported queries, or measure how far a query's embedding, a numerical summary of its meaning, lies from the in-scope examples. A query judged out of scope can be declined, handed to a person or another service, or answered with a request to rephrase, instead of receiving an unreliable or invented answer. The detector tests resemblance to the defined scope, not what the system actually knows, so it needs evaluating on realistic out-of-scope queries.	Reliability Safety Transparency
Runtime Monitoring and Circuit Breakers	Runtime monitoring and circuit breakers observe a deployed AI service and restrict model calls when configured signals breach specified limits. In the closed state, requests pass through and recent failures are counted, while an open breaker rejects requests or provides a configured fallback, such as a cached answer, a simpler model, a rule-based default or referral to a person. After a waiting period, the half-open state permits a few trial calls and closes again only if they succeed. Model-related triggers can include immediately available signals such as input-drift scores (how far incoming data has moved from the training data) or low prediction confidence, since accuracy usually needs later outcome labels. Logs record state changes and triggering signals, supporting review of why the service was restricted without guaranteeing that the fallback is safe.	General Reliability Safety Transparency