Confidence Thresholding

Description

Confidence thresholding creates decision boundaries based on model uncertainty scores, routing predictions into different handling workflows depending on their confidence levels. High-confidence predictions (e.g., above 95%) proceed automatically, whilst medium-confidence cases (e.g., 70-95%) may trigger additional validation or human review, and low-confidence predictions (below 70%) receive extensive oversight or default to conservative fallback actions. This technique enables organisations to maintain automated efficiency for clear-cut cases whilst ensuring appropriate human intervention for uncertain decisions, balancing operational speed with risk management across safety-critical applications.

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.

Reliability

Deploying confidence thresholding in fraud detection systems where high-confidence legitimate transactions (>90%) process immediately, medium-confidence cases (70-90%) undergo additional automated checks, and low-confidence transactions (<70%) require human analyst review, ensuring system reliability through graduated response mechanisms.

Transparency

Using confidence thresholds in automated loan decisions to provide clear explanations to applicants, where high-confidence approvals include simple explanations, medium-confidence decisions provide detailed reasoning about key factors, and low-confidence cases receive comprehensive explanations with guidance on potential improvements.

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.
Implementation complexity increases significantly when managing multiple confidence levels and routing mechanisms, potentially introducing system failures or inconsistencies in how different confidence ranges are handled.

Resources

Research Papers

A Novel Dynamic Confidence Threshold Estimation AI Algorithm for Enhanced Object Detection

Mounika Thatikonda, M. Pk, and Fathi H. Amsaad•Jan 1, 2024

Object detection (OD) is a significant component in the field of computer vision applications, ranging from autonomous vehicles to ensuring security in Department of Defense (DoD) systems. Despite the huge progress that has been made, the accuracy of the models in various situations is limited to static detection parameters like confidence threshold and NMS threshold. The static method of deciding the thresholds may lessen the effect of detections, thereby impacting the mean average precision (mAP) score and the overall efficacy of OD models in real-world scenarios. To overcome this drawback, this study introduced a novel approach that dynamically adjusts the confidence threshold and NMS threshold based on statistical measures of the entire object representation in a data plane. The COCO Dataset was chosen to evaluate the performance of dynamic threshold techniques across five different pre-trained state-of-the-art models like YOLO v5, Faster R-CNN, FCOS, RetinaNet, and SSD300. Among all the models, Faster R-CNN outperformed others with a mAP of 56.11 %. This dynamic approach makes the OD models more reliable by improving the adaptability and robustness of object detection models.

Improving speech recognition accuracy with multi-confidence thresholding

Shuangyu Chang•Jan 1, 2006

Improving the Robustness and Generalization of Deep Neural Network with Confidence Threshold Reduction

Xiangyuan Yang et al.•Jun 2, 2022

Deep neural networks are easily attacked by imperceptible perturbation. Presently, adversarial training (AT) is the most effective method to enhance the robustness of the model against adversarial examples. However, because adversarial training solved a min-max value problem, in comparison with natural training, the robustness and generalization are contradictory, i.e., the robustness improvement of the model will decrease the generalization of the model. To address this issue, in this paper, a new concept, namely confidence threshold (CT), is introduced and the reducing of the confidence threshold, known as confidence threshold reduction (CTR), is proven to improve both the generalization and robustness of the model. Specifically, to reduce the CT for natural training (i.e., for natural training with CTR), we propose a mask-guided divergence loss function (MDL) consisting of a cross-entropy loss term and an orthogonal term. The empirical and theoretical analysis demonstrates that the MDL loss improves the robustness and generalization of the model simultaneously for natural training. However, the model robustness improvement of natural training with CTR is not comparable to that of adversarial training. Therefore, for adversarial training, we propose a standard deviation loss function (STD), which minimizes the difference in the probabilities of the wrong categories, to reduce the CT by being integrated into the loss function of adversarial training. The empirical and theoretical analysis demonstrates that the STD based loss function can further improve the robustness of the adversarially trained model on basis of guaranteeing the changeless or slight improvement of the natural accuracy.

Related Techniques

Name	Description	Assurance Goals
Empirical Calibration	Empirical calibration adjusts a model's predicted probabilities to match observed frequencies. For example, if events predicted with 80% confidence only occur 60% of the time, calibration would correct this overconfidence. Common techniques include Platt scaling and isotonic regression, which learn transformations that map the model's raw scores to well-calibrated probabilities, improving the reliability of confidence measures for downstream decisions.	Reliability Transparency Fairness
Human-in-the-Loop Safeguards	Human-in-the-loop safeguards establish systematic checkpoints where human experts review, validate, or override AI/ML system decisions before they take effect. This governance approach combines automated efficiency with human judgement by defining clear intervention criteria (such as uncertainty thresholds, risk levels, or sensitive contexts) that trigger mandatory human oversight. By incorporating domain expertise, ethical considerations, and contextual understanding that machines may lack, these safeguards help ensure that critical decisions maintain appropriate human accountability whilst preserving the benefits of automated processing for routine cases.	General
Epistemic Uncertainty Quantification	Epistemic uncertainty quantification systematically measures model uncertainty about what it knows, partially knows, and doesn't know across different domains and topics. This technique uses probing datasets, confidence calibration analysis, and consistency testing to quantify epistemic uncertainty (uncertainty due to lack of knowledge) as distinct from aleatoric uncertainty (inherent data uncertainty). Uncertainty quantification enables appropriate deployment scoping, communicating model limitations, and identifying knowledge gaps requiring additional training or human oversight.	Transparency Reliability Safety
Out-of-Domain Detection	Out-of-domain (OOD) detection identifies user inputs that fall outside an AI system's intended domain or capabilities, enabling graceful handling rather than generating unreliable responses. This technique trains classifiers or uses embedding-based methods to recognise when queries require knowledge or capabilities the system doesn't possess. OOD detection enables systems to explicitly decline, redirect, or request clarification rather than hallucinating answers or applying inappropriate reasoning to unfamiliar domains.	Reliability Safety Transparency