This workshop brought together 22 participants with a diverse background from AI, Robotics, HCI, Ubiquitous Computing, Business and Sociology, from across Europe and North America laid the groundwork for a manifesto on Hybrid Human-centered AI systems.
Informed by currently ongoing large initiaves such as the EU-funded Humane AI Net, the Dutch Hybrid Intelligence Center, the Danish Centre for Hybrid Intelligence, and OECD AI policy framework, four pillars of the manifesto emerged: (a) Collaboration and Cooperation, (b) Control & Adaptivity, (c) Transparency & Explainability, and (d) Societal dimensions. For each of these pillars, the workshop resulted in (i) key terminology, (ii) key research questions, (iii) metrics and methodologies, and (iv) benchmarks and challenges.
The above resulted in a solid framework for the Hybrid Human-Centered AI manifesto to be written by the partcipants in the months following the Dagstuhl workshop.
Society is undergoing a revolution in artificial intelligence (AI), with huge potential benefits, but also major risks for individuals and society.
Increasingly, trust in the development, deployment, and the use of AI and autonomous systems concerns not only the technology’s inherent properties, but also the socio-technical systems of which they are part of, that is, the people, organisations, and societal environments in which systems are developed, implemented, and used. Currently, major challenges include the lack of fundamental theory and models to analyse and ensure that systems are aligned with human values and ethical principles, accountable, open to inspection, and understandable to diverse stakeholders. Furthermore, there is no doubt that this technological shift will have revolutionary effects on human life and society.
The goal of this Dagstuhl Perspectives Workshops is to contribute to shape that revolution, to provide the scientific and technological foundations for designing and deploying AI systems that work in partnership with human beings, to enhance human capabilities rather than replace human intelligence. Fundamentally new solutions are needed for core research problems in AI and human-computer interaction (HCI), especially to help people understand actions recommended or performed by AI systems and to facilitate meaningful interaction between humans and AI systems.
Specific challenges include: learning complex world models; building effective and explainable machine learning systems; developing human-controllable intelligent systems; adapting AI systems to dynamic, open-ended real-world environments (in particular robots and autonomous systems); achieving in-depth understanding of humans and complex social contexts; and enabling self-reflection within AI systems.
Expected results (outcome) of the workshop
- Define a coherent research agenda for this rapidly emerging discipline
- Produce a clear narrative on content and urgency of the discipline to influence policy makers
- Trigger scientific innovation across the whole spectrum from fundamental research to practical applications
- Develop synergies across Europe on this emerging research theme and link with similar international initiatives (e.g. at Stanford https://hai.stanford.edu and MIT https://hcai.mit.edu).
- Michel Beaudouin-Lafon (University Paris-Saclay - Orsay, FR) [dblp]
- Mehul Bhatt (University of Örebro, SE) [dblp]
- Stefan Buijsman (TU Delft, NL)
- Mohamed Chetouani (Sorbonne University - Paris, FR) [dblp]
- Ulises Cortés (UPC Barcelona Tech, ES)
- Adam Dahlgren Lindström (University of Umeå, SE)
- Emmanuelle Dietz (Airbus - Hamburg, DE)
- Marko Grobelnik (Jozef Stefan Institute - Ljubljana, SI) [dblp]
- Alípio Jorge (University of Porto & INESC TEC - Porto)
- Antonis C. Kakas (University of Cyprus - Nicosia, CY) [dblp]
- Samuel Kaski (Aalto University, FI) [dblp]
- Janin Koch (INRIA Saclay - Orsay, FR)
- Helena Lindgren (University of Umeå, SE) [dblp]
- Paul Lukowicz (DFKI - Kaiserslautern, DE) [dblp]
- Wendy E. Mackay (INRIA Saclay - Orsay, FR) [dblp]
- Ozlem Ozmen Garibay (University of Central Florida - Orlando, US)
- Janet Rafner (Aarhus University, DK)
- Laura Sartori (University of Bologna, IT)
- John Shawe-Taylor (University College London, GB) [dblp]
- Jacob Sherson (Aarhus University, DK)
- Marija Slavkovik (University of Bergen, NO) [dblp]
- Philipp Slusallek (DFKI - Saarbrücken, DE) [dblp]
- Frank van Harmelen (VU University Amsterdam, NL) [dblp]
- Katharina A. Zweig (TU Kaiserslautern, DE) [dblp]
- artificial intelligence / robotics
- Artificial Intelligence
- AI and Society
- Ethical AI
- Human-centered AI
- Human-Centered Design
- Human-Computer Interaction
- Explainable Machine Learning