activity
20242026
most citedA Multi-Turn Framework for Evaluating AI Misuse in Fraud and Cybercrime Scenarios

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL2026

RealityTest: How People Probe AI Identity and Whether Models Disclose It

Anna Gausen, Sarenne Wallbridge, Bessie O'Dell +2

AI systems are increasingly deployed in conversational settings where users may be uncertain whether they are speaking with a human or an AI. Despite mounting regulatory attention…

cs.CY20261 cited

A Multi-Turn Framework for Evaluating AI Misuse in Fraud and Cybercrime Scenarios

Kimberly T. Mai, Anna Gausen, Magda Dubois +5

AI is increasingly being used to assist fraud and cybercrime. However, it is unclear the extent to which current large language models can provide useful information for complex cr…

cs.HC2026

Disclosure By Design: Identity Transparency as a Behavioural Property of Conversational AI Models

Anna Gausen, Sarenne Wallbridge, Hannah Rose Kirk +2

As conversational AI systems become more realistic and widely deployed, users are increasingly uncertain about whether they are interacting with a human or an AI system. When AI id…

cs.CL2025

Measuring what Matters: Construct Validity in Large Language Model Benchmarks

Andrew M. Bean, Ryan Othniel Kearns, Angelika Romanou +39

Evaluating large language models (LLMs) is crucial for both assessing their capabilities and identifying safety or robustness issues prior to deployment. Reliably measuring abstrac…

cs.CY2024

A Framework for Exploring the Consequences of AI-Mediated Enterprise Knowledge Access and Identifying Risks to Workers

Anna Gausen, Bhaskar Mitra, Siân Lindley

Organisations generate vast amounts of information, which has resulted in a long-term research effort into knowledge access systems for enterprise settings. Recent developments in…