Talk Title
"Responsible AI for Making Interactive Systems Explainable, Personalised, and Accessible"
As intelligent systems become deeply embedded in everyday environments — from smart homes to assistive robots — their increasing complexity risks undermining user understanding, trust, and control. This talk introduces the concept of responsive artificial intelligence, a shift from systems that simply execute actions to systems that actively interpret context, adapt to users, and support human understanding and control.
The talk presents a research journey across three domains: IoT automations, robot-assisted cognitive training, and accessibility validation. It shows how Large Language Models (LLMs) can support semantic interpretation, explain complex behaviours, and generate personalised and meaningful interactions. These capabilities enable a new generation of systems that respond not only to inputs but also to user goals, context, and experience, promoting inclusion, responsible practices, and human oversight.
Drawing on multiple research projects — including human control in smart environments, humanoid robots, and accessible support — the talk discusses how AI systems can be exploited to obtain more transparent, trustworthy, and socially sustainable applications. It highlights design approaches for explaining automations, detecting and resolving conflicts, analysing activation chains, and addressing related security and privacy issues, showing how to obtain user understanding and control in smart environments.
Building on this perspective, the talk explores the transition to Small Language Models (SLMs), which enable more privacy-aware, locally deployable intelligent support. It concludes by outlining design principles and open challenges for responsive intelligent systems, emphasising the need to balance automation with transparency, personalisation with control, and technological power with human values.
