Safety Envelope Testing

Description

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.

Example Use Cases

Safety

Testing autonomous vehicle perception systems at the limits of weather conditions, lighting, and sensor coverage to establish safe operational boundaries and determine when human intervention is required.

Assessing financial trading algorithms under extreme market conditions and volatility to prevent catastrophic losses and ensure system shutdown protocols activate appropriately.

Reliability

Evaluating medical AI diagnostic systems with edge cases near decision boundaries to ensure reliable performance and identify when the system should defer to human specialists.

Limitations

Requires comprehensive domain expertise to identify relevant boundary conditions and edge cases that could affect system safety.
May be computationally expensive and time-consuming, especially for complex systems with high-dimensional operational domains.
Difficult to achieve complete coverage of all possible boundary conditions, potentially missing critical edge cases.
Results may not generalise to novel scenarios that fall outside the tested boundary conditions.
Establishing appropriate safety thresholds and performance criteria requires careful calibration based on domain-specific risk tolerance.

Resources

On the brittleness of AI systems

Research Paper•Andrew J. Lohn•Sep 2, 2020

Analysis of AI system brittleness and the need for improved testing, especially for out-of-distribution performance

Safety Assurance of Artificial Intelligence-Based Systems: A Systematic Literature Review

Research Paper•Antonio V. Silva Neto et al.•Dec 14, 2022

Comprehensive systematic literature review on safety assurance methods for AI-based systems

AMLAS - Assurance of Machine Learning in Autonomous Systems

Documentation

Tool for systematically creating safety cases for machine learning components with guidance through safety envelope testing

System and Safety Analysis with SysAI A Statistical Learning Framework

Research Paper•Yuning He•Jul 12, 2022

NASA technical report on statistical learning framework for system and safety analysis in AI systems

Related Techniques

Name	Description	Assurance Goals
Model Cards	Model cards are standardised documentation frameworks that systematically document machine learning models through structured templates. The templates cover intended use cases, performance metrics across different demographic groups and operating conditions, training data characteristics, evaluation procedures, limitations, and ethical considerations. They serve as comprehensive technical specifications that enable informed model selection, prevent inappropriate deployment, support regulatory compliance, and facilitate fair assessment by providing transparent reporting of model capabilities and constraints across diverse populations and scenarios.	Transparency Fairness Safety
Data Version Control	Data Version Control (DVC) is a Git-like version control system specifically designed for machine learning data, models, and experiments. It tracks changes to large data files, maintains reproducible ML pipelines, and creates a complete audit trail of data transformations, model training, and evaluation processes. DVC works alongside Git to provide end-to-end lineage tracking from raw data through preprocessing, training, and deployment, enabling teams to reproduce any model version and understand exactly how datasets evolved throughout the ML lifecycle.	Transparency Reliability
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
Multi-Accuracy Boosting	An in-processing fairness technique that employs boosting algorithms to improve accuracy uniformly across demographic groups by iteratively correcting errors where the model performs poorly for certain subgroups. The method uses a multi-calibration approach that trains weak learners to focus on prediction errors for underperforming groups, ensuring that no group experiences systematically worse accuracy. This iterative boosting process continues until accuracy parity is achieved across all groups whilst maintaining overall model performance.	Fairness Reliability Transparency
Calibration with Equality of Opportunity	A post-processing fairness technique that adjusts model predictions to achieve equal true positive rates across protected groups whilst maintaining calibration within each group. The method addresses fairness by ensuring that qualified individuals from different demographic groups have equal chances of receiving positive predictions, whilst preserving the meaning of probability scores within each group. This technique attempts to balance the competing objectives of group fairness and accurate probability estimation.	Fairness Transparency Reliability
Exponentiated Gradient Reduction	An in-processing fairness technique based on Agarwal et al.'s reductions approach that transforms fair classification into a sequence of cost-sensitive classification problems. The method uses an exponentiated gradient algorithm to iteratively reweight training data, returning a randomised classifier that achieves the lowest empirical error whilst satisfying fairness constraints. This reduction-based framework provides theoretical guarantees about both accuracy and constraint violation, making it suitable for various fairness criteria including demographic parity and equalised odds.	Fairness Transparency Reliability