Jul 2026· Philosophical transactions. Series A, Mathematical, physical, and engineering sciences· Vol 384 2324· 1 citation· 32 references
Medicine
TL;DR
The reality of deploying artificial intelligence in safety-critical systems, such as autonomous vehicles, medical diagnoses and weather forecasting, is considered, including how an AI's mathematical properties relate to its benefit and risk profile.
Abstract
We consider the reality of deploying artificial intelligence (AI) in safety-critical systems, such as autonomous vehicles, medical diagnoses and weather forecasting. Our discussion is grounded in the mathematical nature of AI systems, including how an AI's mathematical properties relate to its benefit and risk profile. Benefits include the ability to learn models from data even when no physical model exists, increased automation and enhanced speed compared with traditional approaches. Risks of AI include its opaque (mis-)understanding of the world, failures on out-of-distribution inputs, its insatiable appetite for data and computation and the ongoing challenge of aligning the AI's objectives with human values. Such risks are potentially manageable with clear-eyed expectations, and our hope in this work is to clarify what can be expected. This article is part of the theme issue 'Safe, secure and robust AI for safety-critical systems'.
It is asserted that trustworthiness is a systems property, not a single algorithmic feature, and that realising it requires coordinated advances in explainability, uncertainty quantification, human–AI interaction design, failure detection, and domain-specific data governance.
This special issue seeks to address the interconnected challenges of safety, security and robustness in AI in a holistic manner, recognizing that dependable AI systems must be not only performant, but also trustworthy across a wide range of operational conditions.
Ajitha Rajan, D. Higham· Philosophical Transactions o...· 0 citations
It is argued that agentic AI should be approached as a socio-technical design problem, where interfaces, oversight mechanisms, and evaluation practices are as critical as algorithms.
Timothy Merritt, Alejandro Jarabo-Peñas, Juan Bravo-Arrabal et al.· 0 citations
The paper describes an approach to automated dynamic operational alignment in a complex high-risk process, exemplified by a case study on autonomous drilling, and possible sources of AI misalignments are discussed.
R. Mihai, Benoit Daireaux, E. Cayeux· Scientific Reports· 0 citations
This work presents a method for representativeness assessment of AI/ML constituent ODDs in the context of aviation safety assurance and illustrates how statistical distribution comparison methods can support the assessment of representativeness for safety-critical AI applications.
Thomas Stefani, J. Christensen, Elena Hoemann et al.· 0 citations
This thesis first identifies multiple challenges in achieving rapid DevSafeOps in AD development and then proposes several approaches for addressing these challenges across different phases of the DevSafeOps cycle.
Ali Nouri· 0 citations
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