Artificial Intelligence

Artificial Intelligence (AI) represents a next wave in the digital revolution. Already AI is changing the way we

live and work and has great potential for society’s grand challenges. At the same time, developing and integrating AI in a responsible way is not easy. Human-centeredness, value sensitivity and excellence across all AI aspects should pave the way towards augmented intelligence. Such AI is transparent, explainable, fair, socially compatible, and is developed and deployed based on careful consideration of the disruptions AI technology can cause. This track aims to provide a showcase and forum for connecting Dutch research on this theme.

The focal point of the parallel and poster sessions is the presentation of the work of young researchers. Preference will be given to abstracts by postdocs and PhD candidates.

Track chairs:
Charlotte Gerritsen (VU)
Johan Versendaal (Hogeschool Utrecht)

Invited Speakers

Bio Martijn Zoet
Martijn Zoet is a professor (lector) at Zuyd University of Applied
Science in Sittard and holds the chair for the Future Proof Financial.
He obtained his PhD from Utrecht University and was a researcher
at HU university of applied science Utrecht from 2010 to 2014
before joining Zuyd University of Applied Science. In addition,
Martijn is the managing partner of the EDM Competence Centre
that he founded in October 2015. EDM-CC supports corporations,
governments, and institutions to map their data, decision
management, and machine learning challenges and find solutions
to these challenges. His research focus is on business
rules management, decision management, decision mining,
process mining, data mining, machine learning and, FinTech.

Abstract Title: Artificially intelligence in the wild
The development of Generative Adversarial Networks, AlphaGoZero and GPT-2 are all important advances in the field of artificial intelligence. However, which percentages of organizations apply any of the previously mentioned techniques? And are the techniques used to drive business strategy? The answer to the first question is that only a very small part of organizations applies one or more of these techniques. And even a smaller part of organizations applies them to drive business strategy. Still, these breakthroughs provide a temptation to mainly focus on the development of a strategy for AI rather than a strategy with AI. While both are needed to drive the development of AI as well as the adoption by organizations.

The same applies to AI research were a temptation exists to mainly focus on the development of fundamental techniques, principles, and ethics for AI rather than techniques, principles, and ethics that work with AI. An example of this are statements like: “Such AI is transparent, explainable, fair, socially compatible, and is developed and deployed based on careful consideration of the disruptions AI technology can cause.“ The words transparent, fair and explainable have a different meaning when performing fundamental research, philosophical research or applied research. Therefore different viewpoints have to be adopted for each research type. For example, in applied research, a shift from less focus on bias and more focus on harm should be applied. And this is just one of many examples.

The presentation will discuss the challenges and shifts of focus needed to scale and strengthen fundamental as well as applicable AI research.

Bio Judith Masthoff
Professor Judith Masthoff is a chair in Interaction Technology at Utrecht University and in Computing Science at the University of Aberdeen. Her research focusses on personalization: how artificial intelligence systems can automatically adapt to humans. She has applied this in a broad range of areas including recommender systems, intelligent user interfaces, intelligent tutoring systems, persuasive technology, and affective computing. She is Editor in Chief of the User Modeling and User-Adapted Interaction journal and a director of User Modeling Inc., the professional association of user modeling researchers.

Abstract Title: Artificial Intelligence for Emotional Wellbeing
Researchers claim that we are facing a global loneliness epidemic, and that mental illness, anxiety disorders, stress and burnout are on the rise. This talk is about how adaptive AI systems can actively improve emotional wellbeing. We will discuss different ways of doing so, the work already done, the challenges faced, and our vision of a new kind of personalized systems. First, systems can provide emotional support, adapted to the recipient's characteristics such as their personality, affective state, cultural background, and stressors experienced. Second, systems can aid humans to provide emotional support, so mediating emotional support, adapting to both the support giver and recipient. Third, systems can support and motivate people to adopt behaviours that improve their wellbeing and that of others. Fourth, systems can team people up, deciding who are the best placed to provide support and motivation. Finally, systems can improve the wellbeing of groups and not just individuals, monitoring group wellbeing, encouraging and supporting effective group behaviours, and building group identity and cohesion.

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