On Wednesday, the Alan Turing Institute argued that advanced AI should be assessed as part of the systems in which people encounter it. The UK already evaluates frontier models before release, the institute says, yet a model can pass a benchmark and still fail when connected to users, software, staff and operating procedures. Its new report maps five areas for practical work, including cyberattacks, democratic disruption, misalignment and loss of human control. A £2 million research programme and a briefing on agent behaviour aim to develop ways to test those risks in use.

That shift places responsibility inside the institution deploying the tool. If an agent screens applications or manages a public service, reliability depends on who limits its permissions, notices changes, handles an error and keeps a safe fallback available. Whole-system checks can make these decisions visible, but also raise the question of who decides that evidence is enough when people affected by failure rarely take part in the evaluation. The Turing proposes testing observable behaviour in real conditions, involving the people and processes around the technology, then reassessing when either changes. Disputes over risk and power will continue. Each deployment should show who can override the tool, what happens when it stops, and whether the service keeps working.