Sensemaking in Multi-Agent LLM Interfaces: How Users Interpret Transparency and Trustworthiness Cues.
Examines how people interpret agents, disagreement, critique, and debate as signals of transparency and reliability.
University of Queensland
I am a Postdoctoral Research Fellow in Human-Centred AI.
I design and study how humans interact with artificial intelligence (AI) systems. My mission is to improve how appropriately humans trust, contest, and oversee AI systems in decision-making settings, so we can leverage the benefits of this technology while minimising the risks it poses. Broadly, I am interested in the socio-cognitive processes that guide human-AI decision-making, and in designing interaction techniques that support humans in correctly evaluating and relying on AI outputs.
Research programme
How can we support humans in inspecting, evaluating, and intervening to correct AI decisions at scale?
How do humans decide when to accept or question AI decisions, and how might we better support their calibration?
What kinds of information help humans in interpreting and making sense of AI outputs more correctly?
How can we support humans in better evaluating the veracity of (mis)information online?
How do trust and transparency needs change as systems evolve from classical AI, to single-agent, to multi-agent LLMs?
Publications
Examines how people interpret agents, disagreement, critique, and debate as signals of transparency and reliability.
Shows how framing AI decisions as internally or externally caused changes users’ trust responses.
Compares different explanation types for supporting reliance in misinformation assessment.