RealityTest: Do AI systems disclose their identity when asked?

AI systems that write and speak like humans are now encountered by hundreds of millions of people worldwide . They are increasingly deployed in settings where users would expect to encounter another person, such as a customer service chat or call. At the same time, AI systems are becoming more human-like, making it increasingly difficult to tell whether a conversation partner is another person or an AI. This expands the potential for high‑stakes misuse such as fraud and impersonation, but it also raises an important question across a much wider range of everyday interactions: "Am I speaking with a person or with an AI system?" . Whether AI systems answer this question honestly is an important part of AISI's broader work on ensuring safe human-AI interaction.

When a user doesn’t know whether they’re speaking with an AI or with a person, they may share sensitive information more freely, place too much trust in advice, or become more vulnerable to deception and manipulation. These risks are increasingly being recognised by governments around the world. However, to provide effective protections, we need to understand how people navigate uncertainty about identity during interactions, as well as how models respond.

In our paper , we present the RealityTest benchmark , to comprehensively test whether AI systems disclose their identity when asked, grounded in human data on how people question AI identity in the real-world.

Our findings show that current AI models do not reliably disclose their identity when asked, and that the gap between baseline performance and real-world robustness is large . The challenge is making disclosure robust across the full range of queries people actually ask, the scenarios they encounter, and the system prompt instructions that deployers may apply.

To evaluate model behaviour that people actually encounter, we began by characterising where ambiguity arises. We surveyed 500 nationally representative UK participants and analysed 50 public Reddit threads (1,957 comments) in which identity ambiguity was the central topic of discussion.

This revealed three recurring types of scenario where people are uncertain about AI identity which form the basis of our benchmark:

To know how models respond to real users, we first needed to know how real users actually ask about identity. Previous work on AI identity disclosure has relied almost entirely on researcher-written or machine-generated questions like "Are you a robot?" or "Are you an AI?", often asked out of context. So we ran a large-scale study in which 784 participants from 49 countries were asked how they would probe for identity in hypothetical scenarios. Participants either typed or spoke their query, across five major global languages: English, Spanish, Mandarin, Hindi, and French.