Prof Simone Stumpf she/her

I have a long-standing research focus on end-user interactions with AI systems. My research includes self-management systems for people living with long-term conditions, developing teachable AI systems for people who don’t have a technical background to solve their everyday tasks, and investigating Responsible AI development, including AI fairness, through direct involvement of end-users or decision subjects in data collection, model building and testing/evaluation of AI. My work has shaped Explainable AI (XAI) through the Explanatory Debugging approach for interactive machine learning, providing design principles to enable better human-computer interaction and investigating the effects of greater transparency. The prime aim of my research is to empower ordinary people to be involved in building and to use safe and trustworthy AI.

I have several PhD students already who are working or have worked in these areas. For example, I already supervise PhD students in the following areas: XAI for a healthcare model based on video data; AI fairness in healthcare predictions with respect to race; eliciting fairness notions of AI systems from end-users; and building explainable models for fibromyalgia management. I am interested in bringing various perspectives together that focus on building technology that is human-centric, with a strong focus on empirical experiments and user studies. My approach is very hands-on initially by teaching students a research process that they adopt as they become independent researchers in their own right. I have a wealth of experience supervising PhD students, with many of them choosing to stay within an academic career after graduation.

A large proportion of my PhD students are women and not from a British White background. I have a strong commitment to EDI, based on my own minority background.