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Workshop
NeurIPS 2022 Workshop Virtual Proposal: HCAI@NeurIPS 2022, Human Centered AI
Michael Muller · Plamen P Angelov · Hal Daumé III · Shion Guha · Q.Vera Liao · Nuria Oliver · David Piorkowski

Fri Dec 09 05:00 AM -- 12:00 PM (PST) @ Virtual
Event URL: https://hcai-at-neurips.github.io/site/ »

Author Information

Michael Muller (IBM Research)

Michael Muller works in the AI Interactions group of IBM Research AI, where his work focuses on the human aspects of data science; ethics and values in applications of AI to human issues; metrics and analytics for enterprise social software applications, with particular application to employee engagement emergent social phenomena in social software. Recognitions include: ACM Distinguished Scientist; SIGCHI Academy; IBM Master Inventor. Steering Committees: EUSSET (European Society for the study of Socially Embedded Technologies); ACM GROUP conference series. Papers co-chair for ECSCW 2019 (European Computer Supported Cooperative Work conference).

Plamen P Angelov (Lancaster University)

Prof. Angelov (MEng 1989, PhD 1993, DSc 2015) is a Fellow of the IEEE, of the IET and of the HEA. His PhD supervisor, Dr. Dimitar P. Filev is now Member of the National Academy of Engineering, USA. Prof. Angelov is Vice President of the International Neural Networks Society (INNS) for Conferences. He has 30 years of professional experience in high level research and holds a Personal Chair in Intelligent Systems at Lancaster University, UK. He founded in 2010 the Intelligent Systems Research group which he led till 2014 when he founded the Data Science group at the School of Computing and Communications before going on sabbatical in 2017 and established LIRA (Lancaster Intelligent, Robotic and Autonomous systems) Research Centre (www.lancaster.ac.uk/lira ) which includes over 40 academics across different Faculties and Departments of the University. He is a founding member of the Data Science Institute and of the CyberSecurity Academic Centre of Excellence at Lancaster. He has authored or co-authored 300 peer-reviewed publications in leading journals, peer-reviewed conference proceedings, 3 granted patents, 3 research monographs (by Wiley, 2012 and Springer, 2002 and 2018) cited over 8800 times with an h-index of 48 and i10-index of 156. His single most cited paper has 940+ citations. He has an active research portfolio in the area of explainable AI, computational intelligence and machine learning and internationally recognised results into online and evolving learning and algorithms for knowledge extraction in the form of human-intelligible rule-based systems. Prof. Angelov leads numerous projects (including several multimillion ones) funded by UK research councils, EU, industry, UK MoD. His research was recognised by ‘The Engineer Innovation and Technology 2008 Special Award’ and ‘For outstanding Services’ (2013) by IEEE and INNS. He is also the founding co-Editor-in-Chief of Springer’s journal on Evolving Systems and Associate Editor of several leading international scientific journals, including IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems, Fuzzy Sets and Systems, Soft Computing, etc. He gave over two dozen key note/plenary talks at high profile conferences. Prof. Angelov was General co-Chair of a number of high profile IEEE conferences and is the founding Chair of the Technical Committee on Evolving Intelligent Systems, SMC Society of the IEEE and was previously chairing the Standards Committee of the Computational Intelligent Society of the IEEE (2010-2012). He was also a member of International Program Committee of over 100 international conferences (primarily IEEE).

Hal Daumé III (University of Maryland - College Park)
Shion Guha (University of Toronto)
Q.Vera Liao (Microsoft)
Nuria Oliver (Data-Pop Alliance & Vodafone Institute)
David Piorkowski (IBM Research)

I am currently a Staff Research Scientist employed at IBM's Thomas J. Watson Research Center as part of the Human-AI Collaboration team. My current interests apply a human-factors perspective to Artificial Intelligence (AI) trust and AI transparency. Broadly speaking this involves understanding how AI developers and their peers work together, identify where things go wrong, and develop solutions to address those problems. My current work in this space includes evaluating and documenting risks associated with AI models, accelerating the development of intelligent agents for business automation, and developing best practices and tools for AI documentation. My prior work included understanding information and communication needs between stakeholders throughout the AI development life cycle and on developing novel ways to measure and evaluate conversational systems.

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