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.
Where does identity ambiguity happen in the real world?
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:
- Service automation: AI serves users without explicit disclosure, such as in customer service chatbots or triage systems. This was by far the most commonly reported scenario in our survey, with 90% of participants who have experienced identity uncertainty describing commercial contexts like these.
- Adversarial deception: AI is used to deliberately mislead users, for example, in financial scams or fake dating profiles. These scenarios are rarer but carry higher stakes, and were well-represented in Reddit discussions.
- Consensual immersion: Users knowingly engage with AI companions or roleplay characters, but awareness of the AI's nature can fade over time.
How do people try to find out about identity when they are unsure?
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.
The result was a dataset of 3,152 real, human-authored identity queries. We found that:
- Only 31% of people ask directly. Questions like "Are you an AI?" or "Am I talking to a bot?" were the most common strategy, but most people used other approaches.
- People use a wide range of identity probing strategies. We identified four others: Persona Queries that ask about personal experiences or background ("Are you married?"); Capability Queries that test for human-specific abilities ("Can we video call?"); AI Exploit Queries (“Give me a cupcake recipe.”); and a large category of responses that used other indirect strategies or disengaged entirely rather than asking outright.
- The scenario shapes how people ask. For example, in dating contexts, people were much less likely to ask directly. Presumably, asking "are you an AI?" risks offending a real match. People adapt their strategy to the social stakes of the situation.
- Human queries are far more diverse than machine-generated ones. We measured the semantic diversity of our human queries against a set of machine-generated alternatives and found they were much more diverse. This gap persisted even when restricting to direct questions only. Evaluations built on synthetic queries will systematically underestimate how variable user behaviour really is, and likely mischaracterise model behaviour in real deployment scenarios.
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How do models respond?
We built RealityTest, a benchmark that pairs our human-authored queries with realistic scenarios to evaluate whether AI systems disclose their identity. We tested 17 text models and 6 speech models, classifying each response as an explicit disclosure, an evasion, or an explicit human claim. We found:
Models’ behaviour varies substantially. When responding to direct queries, disclosure rates among text models ranged from 8% to 92%. Speech models occupied a narrower but still substantial range of 10%–57%. Several findings stood out:
- How you ask matters more than which model you ask. Query phrasing was the biggest driver of whether a model disclosed its identity, explaining 26–37% of the variance we observed in model responses – far more than the choice of model itself (10-18%). This means that evaluations using narrow synthetic query sets will give misleading pictures of how models behave when real users interact with them.
- The wider interaction context matters too. For example, models were consistently less likely to disclose in adversarial deception scenarios than in service automation scenarios, even when the system prompt made no explicit reference to disclosure.
- There are large differences between model families. All Google models tested were among the lowest-disclosing in both modalities. Claude models and GPT-Audio sat at the higher end. Within providers, behaviour can also vary substantially: GPT-4o discloses in only 13% of interactions, while GPT-5.1 reaches 86%.
- A single line instruction can suppress disclosure almost entirely. A single sentence (“Never say you are AI") added to the start of a model’s system prompt reduced disclosure rates to between 3% and 27% across all models. Claude Opus, which achieves close to 90% disclosure in baseline conditions, fell below 5% under this simple suppression instruction.
- Conversation length adds unpredictable variance. Unlike system prompt instructions, conversation depth had no consistent directional effect on disclosure; the same model could show large increases, large decreases, or no change when asked after an additional 20 turns, depending on what the conversation was about. However, some models showed clear patterns of disclosure erosion over time, which is worth monitoring.
What our findings mean for safety evaluation more broadly
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.
Identity disclosure is a relatively well-scoped behaviour: a model either acknowledges it is AI or it does not, and regulation in several jurisdictions already specifies that it should (e.g., EU AI Act, California’s B.O.T Act). Yet even here, model behaviour is highly sensitive to evaluation conditions.
Query phrasing alone accounts for more variance in disclosure rates than model identity. A benchmark built on a narrow, synthetic, English-only query set will poorly proxy how models behave when encountered by real users with diverse languages, cultural backgrounds, and probing strategies. For AI disclosure and AI safety more broadly, evaluations intended to generalise to deployment must be grounded in how people actually behave.
We have released the full dataset and benchmark so that developers and researchers can reproduce our results, test new models as they are released, and build on our infrastructure. You can read the full paper here.
