Towards a compositional semantics for quantitative confidence assessment in assurance arguments

2026-05-21 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

A new confidence semantics addresses the lack of operational semantics for deriving assurance confidence in widely used notations like Goal Structuring Notation (GSN). This approach represents argument elements as Subjective Logic (SL) opinions and maps relations to SL operators, effectively transforming the argument into an analyzable confidence network. It provides a uniform, compositional method for overall confidence assessment, handling incomplete, conflicting, or subjective evidence. Key features include explicit warrants, principled context handling, preserved provenance, and compatibility with GSN, along with practical guidance for implementation.

Key takeaway

For AI Architects designing safety-critical systems, this approach offers a rigorous method to quantify and propagate confidence in assurance arguments, moving beyond binary truth values. You can integrate its compatibility with GSN to incorporate quantitative confidence assessment into existing frameworks. This enhances trust in system properties and ensures more robust system evaluations.

Key insights

A compositional semantics uses Subjective Logic to model assurance argument elements and relations as a confidence network.

Principles

• Assurance arguments need operational semantics for confidence.
• Subjective Logic combines opinions under uncertainty.
• Argument elements map to SL opinions and operators.

Method

Represent argument elements as Subjective Logic opinions and map relations between elements to SL operators to model confidence flow, forming an analyzable confidence network.

In practice

• Assess overall confidence in GSN-based assurance arguments.
• Handle incomplete, conflicting, or subjective evidence.

Topics

• Assurance Arguments
• Goal Structuring Notation
• Subjective Logic
• Confidence Assessment
• Operational Semantics
• AI Safety

Best for: Research Scientist, AI Scientist, AI Architect, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.