2022 Phase

PHASE 2022 brought academia and industry together in Bristol to examine autonomous systems with humans in or on the loop, across human factors, architecture, ethics, trust and robotics.

Date/location: 28-30 June 2022, Bristol, UK

Website: phase2022.org

We are very pleased to announce the Perspectives in Hybrid Autonomous Systems Engineering (PHASE 2022) in-person symposium, which will bring together experts from academia and industry to discuss and debate what the next generations of autonomous systems will look like - systems that must be designed to have the human in or on the loop. Themes will range across human factors, systems, architecture, ethics, trust, robotics and autonomy. The 3-day programme will include a variety of keynote and contributing speakers, poster sessions, a conference banquet and drinks reception, all hosted in Bristol between 28-30 June 2022.

PLENARY SPEAKERS (CONFIRMED)

Missy Cummings (Duke University) Human-Computer Role Allocation in Safety Critical Systems with Embedded AI Verena Hafner (Humboldt-Universität zu Berlin) Prerequisites for an Artificial Self Simon Powers (Edinburgh Napier University) Working together and valuing each other – building deeply cooperative socio-technical systems Amanda Prorok (University of Cambridge) Deconfliction in Multi-Unit Systems: Communication, Explanation, and Fairness Sarvapali Ramchurn, (University of Southampton) Trustworthy Human-Machine Teaming Sarah Sharples (UK Department for Transport) Hybrid Autonomous Systems Engineering for Transport Anna Trujillo (NASA Langley Research Center) True Partnership: An Actual Human Machine Team Alan Winfield (UWE Bristol) The birth of a new standard: IEEE 7001-2021 on Transparency of Autonomous Systems Dandan Zhang (University of Bristol) Human-Robot Shared Control for Robotic Surgery Across Scales

Conceptual illustrations

The illustrations below were generated with AI. They are conceptual and do not document actual people, events or experimental results.

Conceptual illustration generated with AI; it does not depict actual individuals, events or experimental results.