Dinara Talypova "Human Autonomy and the Meaning of Productivity in Interaction with AI"

This talk can be viewed via zoom. (Note: this talk will be recorded)

Today, AI – from chatbots to agentic systems – is accelerating the way people work and carry out everyday activities. Its effectiveness, and with it our productivity, are typically judged by what is easy to count: time saved, tasks completed, suggestions accepted. Yet a highly relevant but insufficiently operationalized question is how people experience this process – and whether their actions still feel, and remain, their own. 

In my doctoral research, I reconsider the meaning of productivity – beyond efficiency, but with psychological needs in mind. And I do it through the lens of human autonomy. Autonomy shapes human well-being and motivation, which is a value in itself; it affects the quality of our work, not just the quantity. For all its relevance, autonomy in HCI remains vaguely defined and reduced to related constructs such as agency or control. It is often operationalized at the interface level through more choice or override options – an important facet, but only one of several. 

In this talk, I will present my published work on multifaceted autonomy – positive and negative liberty – and what it means for AI design and the future of work. My postdoctoral project goes further, asking how continuous interaction with AI shapes human autonomy over time and what consequences it has for AI alignment research. The objectives are to develop shared conceptual standards and a measurable approach, to trace how AI affects autonomy longitudinally, and to derive design mechanisms for intelligent systems that prioritize meaning over efficiency alone. The result would give designers and regulators a more concrete target than the abstract “respect for human autonomy” in current AI policies.

Bio:
Dinara Talypova is a PhD candidate in the Intelligent User Interfaces group at IT:U Austria and a visiting researcher at the AI Futures Lab, TU Delft. Grounded in cognitive science and HCI, her research began with digital disruptions, multitasking, and attention, and now addresses how interaction with AI shapes human autonomy. Her goal is to design AI systems that support human productivity without undermining autonomy. To this end, she decomposes autonomy into distinct, measurable components – work recognised with Best Paper Honorable Mentions at CHI 2026 and AutomotiveUI 2026.