Designing User interactions around deep learning for applications that define everyday habils (eg diet, lifestyle, shopping)

For AI to be useful, acceptable, and attractive, it is essential to understand user needs and reason for Machine Learning (ML) models with HCI research. This understanding would lead to using data efficiently, effectively, and transparently to answer user needs with ML models that are designed to learn from user interactions. Our research aims to empower the end users with interactive control over the AI systems which they use everyday (such as applications for mental/physical wellbeing, work/leisure/social support), and to communicate how the system works and learns with adaptive explainable interfaces.

Research Q1 - What are the ideal places to place AI as a tool to better support people's everyday life (eg. mental/physical wellbeing, work/leisure/social support)?
Research Q2 - On identifying the needs, how might we build deep models mirroring user interactions?
Research Q3 - What are the benefits of user interactions with intelligent systems? Does this result in a positive experience and explainable AI?
Research Q4 - Can we build a framework for user interactions around deep learning that could be adapted to different applications?

We plan to conduct user interviews and observations with end users and HCI/AI domain experts to find insights into building ML models that mirror user interactions. We will also use quantitative surveys and AMT studies for further understanding and modelling. By identifying the implications to design AI as an interactive tool to better people's everyday life and testing them with research prototypes, we aim to evaluate the research prototypes and construct guidelines for human-algorithm interactions.