From Trust-by-Design to Continuous Trust in the Era of Artificial Intelligence

2026-10-05

AI Summary

This study addresses the erosion of trust in AI systems caused by human errors and device failures during operation, which traditional static verification struggles to mitigate. Moving beyond the limitations of “trust-by-design,” this work pioneers a “continuous trust” theoretical framework spanning the entire system lifecycle. By integrating formal methods, compliance checking, and certification techniques, the proposed framework elucidates mechanisms for dynamically sustaining external stakeholder trust, illustrated through a business refund scenario. This research establishes the theoretical foundations for continuous trust and delineates future directions for developing formal tool support.

Abstract

Trust is crucial in the digital world, particularly in the age of systems based on artificial intelligence. Traditionally, system correctness is demonstrated by proving that the system satisfies the required properties. However, digital systems involve humans, who can make mistakes, break rules, or even intentionally cheat the system. Technical devices may also malfunction or cause errors. Therefore, external stakeholders need to trust a system throughout its operation. To address this challenge, we propose the concept of continuous trust. In this paper, we present a detailed explanation of this concept, based on a running example of a business's refund department. The paper concludes with a discussion of related work, such as compliance checking and certification, as well as an outlook on further questions regarding the formalization and the necessary tool support.

Problem

• Continuous Trust
• Artificial Intelligence
• Digital Systems
• System Correctness
• Trust-by-Design

Innovation

• Continuous Trust
• Artificial Intelligence
• Trust-by-Design
• Compliance Checking
• Formalization