Can we trust the systems we now rely on? AI drives decisions in cars, hospitals and infrastructure, but hidden failures expose the limits of current safety models, raising urgent questions of trust. AI drives decisions in cars, hospitals and infrastructure, but hidden failures expose the limits of current safety models, raising urgent questions of trust. Article by Adam Green, freelance journalist As a speck of dust lands on a camera lens, inside the neural network interpreting the road ahead, a 30mph speed limit sign registers as 70. The vehicle, trusting the system entirely, accelerates. “When vehicles are making autonomous decisions, we just let them drive,” observes Luca Arnaboldi, Assistant Professor of Cyber Security at the University of Birmingham. “But how do we ensure we can trust them?” With automated systems taking on increasingly high-stakes decisions across society — from vehicles to hospitals, financial systems, and critical infrastructure such as energy, transport, and communications — society requires new risk frameworks that put dependability at the centre, ensuring AI remains trustworthy even in the face of uncertainty and failure. Despite the growing sense that AI is beginning to “reason”, its decisions are fundamentally statistical: models learn by absorbing vast amounts of data into general patterns and predicting what’s most likely, on average, for any request, whether or not that input actually makes sense. In a vehicle travelling at speed, where accidental or adversarial variations far exceed what AI can be trained or tested on, small errors don’t stay small. A sticker on a stop sign, worn lane markings, dust on a lens, a spurious LiDAR reflection: each deviation is trivial for a human observer, but sufficient to push an AI system into misreading a sign, drifting out of lane, or failing to brake at exactly the wrong moment. These kinds of real-world