TL;DR
This dissertation addresses this accessibility gap by designing Human–AI collaboration across diverse perception and behavior by exploring two complementary thrusts: legible oversight of AI work and actionable AI guidance for human work.
Artificial intelligence (AI) for Science is often described as a progression from human-directed assistance toward increasingly autonomous AI scientists. Human-AI collaboration offers a further mode in which human and AI contributions substantively shape scientific reasoning, judgments, and the course of inquiry. Exist...
A prerequisite for deploying robots alongside people is the ability to exchange information fluidly across multiple modalities: robots must interpret implicit social signals, receive explicit instructions, and communicate back in ways non-experts can understand. A key challenge is that humans naturally exchange informa...
Generative AI (GenAI) applications are increasingly adopted in co-creative workflows, taking an active role in design and development tasks. GenAI offers unique opportunities through content generation and synthesis across resources, yet it can lead to losses in co-creativity, due to limited collaborative interactions...
Ref referential uncertainty is studied in a collaborative puzzle task where a human Helper instructs an AI Worker to place pieces, showing that a separately elicited belief distribution over candidate pieces is better calibrated and better discriminates correct from incorrect placements than raw action-token probabilit...
The aim of this white paper is to provide a clear and practical guide for designing human-AI collaboration that is effective, human-centred, and responsible.
Two key design dimensions of human-AI collaboration (autonomy and initiative) are examined and the collaboration patterns that they generate are explored, including Instruction, Delegation, Assistance, and Co-creation.
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