How smart should your AI be? How should you interact with it? Do you need to understand how it makes decisions? When does AI augment human work and when does it make human work more challenging. These are the questions we are asking.  

We are looking at AI from intelligent data fusion systems, artificial intelligent home agents (Alexa, Google Home, personal assistants and chatbots), automated vehicles (including robots to clean your home), algorithmic trading in finance and in artificially intelligent decision aids.

Here are some of our projects and publications!

Last Updated: Dec 23, 2021

Designing Expressive Robotic Motions using the Framework of Laban Efforts to Understand Perception of Robot Personality

Duration: 2020 - Present

Currently working on a human-robot interaction project which aims to understand the impact of expressive robotic motions on people’s perception of robot personality. The design of robotic motion will be based on the framework of Laban Efforts, which has been used in several other domains including theatre, dancing, computer science, and psychology. Using an online survey and a series of videos, people’s perceptions of robot’s personality will be collected for different combinations of movement features. In addition, impact of self reported personality traits and attitudes towards robots will be analyzed to explore potential relationships between humans and robots. This project focuses on addressing the research problem of how people ascribe personality traits to domestic service robots, with the intention to inform robotic design processes of the future to attain more effortless and intuitive interaction.

Sponsors and Partners:

  • NSERC, Discovery Grant

Cognitive Work Analysis based Explainable AI (PhD Dissertation)

Duration: Jan 2019 - Aug 2021

I am currently exploring how Cognitive Work Analysis (CWA) can be used to improve AI systems with a focus on explainability and interpretability in the context of loan assessment and lending. I use CWA to model expert decision-making and design explanation interfaces to support human decision-making and improve user experience with AI systems.

Sponsors and Partners:

  • NSERC

Development of an Artificial Intelligence intervention system for prevention, diagnosis and treatment of stress injury, utilizing psychophysiological measures, biofeedback and resilience training

Duration: 2018 - 2020

Currently working on a multi-phase, multifaceted project that combines the development of an Artificial Intelligence predictive and proactive intervention system for prevention, diagnosis and treatment of stress injury; Understanding and influencing individual reactions to injurious stress utilizing Machine Learning, psychophysiological measures and biofeedback systems; and resilience training and enhancement of human performance under conditions of acute injurious stress.

Publications:

  1. Dikmen*, M., Burns, C.M. (2017). Trust in autonomous vehicles: The case of Tesla Autopilot and Summon. 2017 IEEE International Conference on Systems, Man and Cybernetics. 1093-1098. 10.1109/SMC.2017.8122757
  2. Li*, Y., Wang, X., and Burns, C.M. (2017). Ecological Interface Design for financial trading: Trading performance and risk preference effects. 2017 IEEE International Conference on Systems, Man and Cybernetics. 600-605. 10.1109/SMC.2017.8122672
  3. Dikmen*, M., and Burns, C.M. (2016). Autonomous driving in the real world: Experiences with Tesla Autopilot and Summon.  8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 225-228.
  4. Li*, Y., Hu, R., Burns, C.M. (2016). Representing stages and levels of automation on the decision ladder: The case of automated financial trading. Proceedings the 2016 Annual meeting of the Human Factors and Ergonomics Society. 328-333.
  5. Chin*, J., Li*, Y., Burns, C. (2018). User perspectives of conversational agents across lifespan: Being assistive, but not too smart. Poster presented at the Cognitive Aging Conference 2018, Atlanta, GA.