Assessing Confidence in Assurance Cases with Assurance 2.0

Core Concepts

Assurance cases should provide indefeasible justification for the decision to deploy a system or service. Confidence in an assurance case cannot be reduced to a single attribute or measurement, but should be based on positive, negative, and residual doubt perspectives.

Abstract

The report discusses the structure and assessment of assurance cases developed using the Assurance 2.0 approach. Key points:

Assurance cases are organized into a structured argument, with claims supported by evidence and reasoning steps. Assurance 2.0 cases use a limited set of five argument blocks to structure the case.

Positive perspectives assess the soundness of the argument, interpreting it as an informal logical proof. Soundness requires that evidence incorporation steps are well-supported, and that interior reasoning steps are deductively valid.

Probabilistic valuations can be used to augment the logical soundness assessment, aggregating probabilities through the argument to provide a numerical measure of confidence. These are applied only to sound cases.

Negative perspectives involve identifying and resolving doubts and defeaters to the case. Systematic methods can be used to discover potential defeaters.

Residual doubts that cannot be fully resolved must be acknowledged and their risks assessed. Conscious judgments about acceptable residual risks are recorded in the case.

The report outlines how the Clarissa prototype tool supports these different confidence assessment perspectives.

Stats

"An assurance case is intended to provide justifiable confidence in the truth of its top claim, which typically concerns safety or security."
"Confidence cannot be reduced to a single attribute or measurement."
"Positive Perspectives consider the extent to which the evidence and overall argument of the case combine to make a positive statement justifying belief in its claims."
"Negative Perspectives record doubts and challenges to the case, typically expressed as defeaters, and their exploration and resolution."
"Residual Doubts: the world is uncertain so not all potential defeaters can be resolved."

Quotes

"Assurance is the process of developing claims and collecting evidence about a system and its environment and using these in support of an argument to justify (or reject) deployment of the system on the grounds of safety, security, or other designated critical properties."
"An assurance case serves (at least) two different audiences having different goals."
"Assurance 2.0 follows this approach and the Clarissa/asce tool provides a graphical user interface for the construction of graphical arguments."

Key Insights Distilled From

Assessing Confidence with Assurance 2.0

by Robin Bloomf... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2205.04522.pdf

Deeper Inquiries

How can the Assurance 2.0 approach be extended to handle more complex or open-ended systems, such as those using AI/ML components?

In order to extend the Assurance 2.0 approach to handle more complex or open-ended systems, particularly those incorporating AI/ML components, several adaptations and considerations can be made:

Specialized Theories and Models: Develop specific theories and models that address the unique characteristics and challenges of AI/ML systems. These theories should encompass the behavior, learning processes, decision-making algorithms, and potential risks associated with AI/ML components.

Probabilistic Reasoning: Given the inherent uncertainty and probabilistic nature of AI/ML systems, integrate probabilistic reasoning methods into the assurance case argument. This involves assessing the confidence levels and uncertainties associated with the AI/ML components' performance and outcomes.

Defeater Analysis for AI/ML: Implement advanced defeater analysis techniques tailored to AI/ML systems. This involves identifying potential failure modes, biases, data quality issues, and other factors specific to AI/ML that could undermine the system's reliability and safety.

Continuous Learning and Adaptation: Recognize that AI/ML systems evolve over time through learning and adaptation. Assurance 2.0 should accommodate the dynamic nature of AI/ML components, allowing for ongoing validation, monitoring, and updating of assurance cases as the system learns and improves.

Interdisciplinary Collaboration: Foster collaboration between assurance experts, AI/ML specialists, domain experts, and ethicists to ensure a comprehensive and holistic approach to assurance. This multidisciplinary perspective can help address the diverse challenges posed by complex AI/ML systems.

What are the potential limitations or drawbacks of the rigorous logical interpretation of assurance cases advocated in Assurance 2.0?

While the rigorous logical interpretation of assurance cases in Assurance 2.0 offers several benefits, such as clarity, consistency, and systematic evaluation, there are potential limitations and drawbacks to consider:

Complexity and Overhead: The strict adherence to logical validation and soundness can introduce complexity and overhead in developing assurance cases, especially for large and intricate systems. The detailed justification and formal reasoning required may increase the time and effort needed for case construction.

Subjectivity in Interpretation: Despite the logical framework, there can be subjectivity in interpreting the evidence, constructing arguments, and assessing deductive validity. Different evaluators may have varying interpretations, leading to potential inconsistencies in assurance judgments.

Inflexibility in Handling Uncertainty: The logical approach may struggle to effectively address uncertainties, ambiguities, and incomplete information common in real-world systems. Assurance 2.0's emphasis on deductive reasoning may not adequately capture the probabilistic nature of certain risks and scenarios.

Limited Scope of Application: The rigid structure of Assurance 2.0 may not be suitable for all types of systems, particularly those with emergent properties, non-deterministic behaviors, or rapidly evolving technologies like AI/ML. Adapting the approach to accommodate such systems may pose challenges.

Resource Intensive: The meticulous validation and verification processes inherent in the logical interpretation of assurance cases can be resource-intensive, requiring specialized expertise, tools, and time. This could potentially limit the scalability and practicality of Assurance 2.0 for certain projects.

How can the confidence assessment methods described be integrated with other system design and development processes, such as hazard analysis or requirements engineering?

Integrating the confidence assessment methods outlined in Assurance 2.0 with other system design and development processes, such as hazard analysis or requirements engineering, can enhance the overall assurance and reliability of the system. Here are some strategies for integration:

Cross-Referencing Assurance Artifacts: Establish clear cross-references between assurance cases, hazard analysis reports, and requirements specifications. Ensure that the confidence assessments align with the identified hazards, safety requirements, and system functionalities.

Traceability and Impact Analysis: Implement traceability mechanisms to link confidence assessments to specific system components, hazards, and requirements. Conduct impact analysis to understand how changes in confidence levels affect hazard mitigation strategies and compliance with requirements.

Collaborative Workshops and Reviews: Organize collaborative workshops and reviews involving assurance experts, hazard analysts, and requirements engineers. Facilitate discussions on the interplay between confidence levels, identified hazards, and system requirements to ensure a comprehensive understanding.

Automated Tool Integration: Explore the integration of automated tools that support confidence assessment, hazard analysis, and requirements engineering. Utilize tools that can generate traceability matrices, perform consistency checks, and automate the validation of assurance artifacts.

Iterative Assurance Process: Embrace an iterative assurance process that incorporates feedback from hazard analysis and requirements engineering activities. Continuously refine confidence assessments based on new insights, updated requirements, and evolving hazard scenarios to maintain alignment with system development.

By integrating confidence assessment methods with hazard analysis and requirements engineering processes, organizations can establish a robust framework for ensuring the safety, security, and reliability of complex systems while meeting regulatory standards and stakeholder expectations.