Networking Event on Human-Centred AI by Bart Lamiroy at Université Reims Champagne-Ardenne

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Bart Lamiroy from CReSTIC, Université Reims Champagne-Ardenne, shares insights on Digital and Emerging Technologies, focusing on Human-centred AI. The institution's strong expertise in Machine Learning, Digital Humanities, and ethical development of technologies is highlighted. Contact details and further project ideas are provided for collaboration opportunities.

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PRESENTATION TITLE (edit) Bart Lamiroy Universit Reims Champagne-Ardenne Networking event on Digital and Emerging Technologies and Human-centred AI 24 January, 2022

Short introduction of the institution CReSTIC is a research unit of the University of Reims Champagne-Ardenne it supports research and innovation activities in digital sciences. CReSTIC hosts approximately 120 people, including over 70 permanent faculty. It is structured in two departments: Computer Science" and "Automation, Control and Signal Processing" involving 9 research teams. The fundamental activities of these teams, such as image analysis, knowledge modeling and extraction, networks, automation, signal processing are involved in multi- and interdisciplinary development actions, particularly in engineering for health, smart agriculture, smart industry, education and society, communicating vehicles and intelligent buildings. CReSTIC is the driving force behind numerous national (PIA, ANR) and international (H2020) academic projects. It is also a major player in innovation in the French Grand Est Region, through its research and industrial transfer actions in partnership with large national groups, but also innovative SMEs.

Expertise Strong expertise in various Machine Learning and IA research topics Focused research team on Human-centered Digital Technologies Long standing track record in Digital Humanities

Topic and project idea Cluster 4 - Destination 6: A Human-centred and Ethical Development of Digital and Industrial Technologies No particular project idea but strong motivation to contribute Machine Learning expertise to broader contexts related to the call. Currently involved in research related to bias detection, interpretation ambiguity and Human impact of Machine Learning labelling errors.