Human-AI Joint Task Performance: Learning from Uncertainty in Autonomous Driving Systems
Tuesday 29 October 2024, 2:00pm to 4:00pm
Venue
CHC - Charles Carter A05 - View MapOpen to
Postgraduates, StaffRegistration
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Research Seminar
High uncertainty tasks such as making a medical diagnosis, judging a criminal justice case and driving in a big city have a very low margin for error because of the potentially devastating consequences for human lives. In this paper, we focus on how humans learn from uncertainty while performing a high uncertainty task with AI systems. We analyze Tesla's autonomous driving systems (ADS), a type of AI system, drawing on crash investigation reports, published reports on formal simulation tests and YouTube recordings of informal simulation tests by amateur drivers. Our empirical analysis provides insights into how varied levels of uncertainty tolerance have implications for how humans learn from uncertainty in real-time and over time to jointly perform the driving task with Tesla's ADS. Our core contribution is a theoretical model that explains human-AI joint task performance.
Speaker
Manchester University
Panos Constantinides is Professor of Digital Innovation and Digital Learning Lead for Executive Education at Alliance Manchester Business School. He holds a PhD from the Judge Business School at the University of Cambridge and he is a Fellow of the Cambridge Digital Innovation Centre. Panos leads the Digital Transformation Research Group at Alliance Manchester Business School. Panos is also one of the co-founders of the European Digital Platforms Research Network (EU-DPRN).
Contact Details
| Name | Teresa Aldren |
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