Jackknife Resampling

Reliability Transparency Fairness

Description

Jackknife resampling (also called leave-one-out resampling) assesses model stability and uncertainty by systematically removing one data point at a time and retraining the model on the remaining data. Unlike bootstrapping which samples with replacement, jackknife creates n different models by excluding each of the n data points once. This systematic approach reveals how individual points influence results, provides robust estimates of prediction variance, and identifies unusually influential observations that may be outliers or leverage points affecting model reliability.

Example Use Cases

Reliability

Evaluating how removing individual countries from a global climate model affects predictions, identifying which regions have outsized influence and providing robust uncertainty estimates for climate projections used in policy decisions.

Transparency

Providing transparent uncertainty estimates in medical risk prediction by showing how individual patient records influence model predictions, enabling clinicians to understand prediction stability and confidence intervals for treatment decisions.

Fairness

Ensuring fair model evaluation in hiring algorithms by systematically testing how removing candidates from different demographic groups affects model performance, revealing whether certain populations disproportionately influence the model's behaviour.

Limitations

Extremely computationally intensive for large datasets, requiring training of n separate models for n data points, making it impractical for datasets with thousands or millions of observations.
May underestimate uncertainty compared to bootstrapping or other resampling methods, as it provides only n different samples rather than a broader exploration of the data distribution.
Assumes that removing single data points provides meaningful insights into model stability, which may not hold when multiple correlated observations drive model behaviour.
Can be sensitive to the choice of performance metric used for evaluation, as different metrics may show different patterns of sensitivity to individual data points.
Provides limited insight into model behaviour on truly novel data, as each jackknife sample is only minimally different from the full training set.

Resources

scikit-learn model_selection.LeaveOneOut

Documentation

Scikit-learn implementation of leave-one-out cross-validation for jackknife resampling

Cross-validation: evaluating estimator performance

Documentation

Comprehensive guide to cross-validation methods including leave-one-out in scikit-learn

Related Techniques

Name	Description	Assurance Goals
Runtime Monitoring and Circuit Breakers	Runtime monitoring and circuit breakers establish continuous surveillance of AI/ML systems in production, tracking critical metrics such as prediction accuracy, response times, input characteristics, output distributions, and system resource usage. When monitored parameters exceed predefined safety thresholds or exhibit anomalous patterns, automated circuit breakers immediately trigger protective actions including request throttling, service degradation, system shutdown, or failover to backup mechanisms. This approach provides real-time defensive capabilities that prevent cascading failures, ensure consistent service reliability, and maintain transparent operation status for stakeholders monitoring system health.	Safety Reliability Transparency
Demographic Parity Assessment	Demographic Parity Assessment evaluates whether a model produces equal positive prediction rates across different demographic groups, regardless of underlying differences in qualifications or base rates. It quantifies fairness using metrics like Statistical Parity Difference (the absolute difference in positive outcome rates between groups) or Disparate Impact ratio (the ratio of positive rates). Unlike techniques that modify data or models, this is purely a measurement approach that highlights when protected groups receive favourable outcomes at different rates, helping organisations identify and document potential discrimination.	Fairness
Reject Option Classification	A post-processing fairness technique that modifies predictions in regions of high uncertainty to favour disadvantaged groups and achieve fairness objectives. The method identifies a 'rejection region' where the model's confidence is low (typically near the decision boundary) and reassigns predictions within this region to benefit underrepresented groups. By leveraging model uncertainty, this approach can improve fairness metrics like demographic parity or equalised odds whilst minimising changes to confident predictions, thus preserving overall accuracy for cases where the model is certain.	Fairness Reliability Transparency
Safety Envelope Testing	Safety envelope testing systematically evaluates AI system performance at the boundaries of its intended operational domain to identify potential failure modes before deployment. The technique involves defining the system's operational design domain (ODD), creating test scenarios that approach or exceed these boundaries, and measuring performance degradation as conditions become more challenging. By testing edge cases, environmental extremes, and boundary conditions, it reveals where the system transitions from safe to unsafe operation, enabling the establishment of clear operational limits and safety margins for deployment.	Safety Reliability
Prototype and Criticism Models	Prototype and Criticism Models provide data understanding by identifying two complementary sets of examples: prototypes represent the most typical instances that best summarise common patterns in the data, whilst criticisms are outliers or edge cases that are poorly represented by the prototypes. For example, in a dataset of customer transactions, prototypes might be the most representative buying patterns (frequent small purchases, occasional large purchases), whilst criticisms could be unusual behaviors (bulk buyers, one-time high-value customers). This dual approach reveals both what is normal and what is exceptional, helping understand data coverage and model blind spots.	Explainability Fairness
Layer-wise Relevance Propagation	Layer-wise Relevance Propagation (LRP) explains neural network predictions by working backwards through the network to show how much each input feature contributed to the final decision. It follows a simple conservation rule: the total contribution scores always add up to the original prediction. Starting from the output, LRP distributes 'relevance' backwards through each layer using different rules depending on the layer type. This creates a detailed breakdown showing which input features helped or hindered the prediction, making it easier to understand why the network made a particular decision.	Explainability Transparency

Tags

Applicable Models:

Assurance Goal Category:

Uncertainty Quantification

Data Requirements:

Access To Training Data

Data Type:

Evidence Type:

Quantitative Metric

Prediction Interval

Expertise Needed:

Lifecycle Stage:

Model Development

System Deployment And Use

Technique Type: