Special Interest Groups

Our special interest groups (SIGs) are interdisciplinary groups of three or more researchers from at least two faculties, that work on subthemes relevant to the focus area Human-centered Artificial Intelligence.

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Autonomous Intelligent Systems

Autonomous Intelligent Systems are AI software systems that act independently of direct human supervision, e.g., self-driving cars, UAVs, smart manufacturing robots, care robots for the elderly and virtual agents for training or support.
Such systems need to be able to make safe, rational and human values-compatible decisions in unforeseen circumstances. Their decision making should be understandable by human users and collaborators, to ensure the necessary trust on behalf of the human users. This SIG investigates symbolic and sub-symbolic models, software tools, and computational techniques to specify, design and develop Autonomous Intelligent Systems. 

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Natural Language and AI 

This SIG broadly concerns topics at the intersection of natural language and AI. The SIG covers all aspects of computational linguistics (CL) and natural language processing (NLP), their applications to technology, as well as connections to cognitive modelling and psychology. Of particular interest are methods that combine traditional rule-based or model driven techniques with modern statistical approaches to CL and NLP.

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AI, Ethics and Law

This SIG investigates AI, ethics and law in two dimensions: legal and ethical problems related to the adoption of AI technologies; and the application of AI to ethical and legal problems. The former dimension covers topics like privacy, responsibility & liability, non-discrimination, and transparency of algorithmic decision making. The latter divides into (i) AI support for humans in their legal or ethical decision making, and (ii) making autonomous computer systems behave in ethically or legally responsible ways.

Knowledge Representation and Reasoning

This SIG is concerned with the representation of knowledge and the formalisation of reasoning. Knowledge representation and reasoning covers a wide range of research topics such as modeling of reasoning in specific domains, automated theorem proving, logical knowledge bases and the actual human reasoning in specific reasoning tasks. Of special interest is the relation between symbolic and sub-symbolic representations and the interaction between automated reasoning and data-driven learning techniques.

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Social and Cognitive Modeling

This SIG investigates human behavior and thought to understand humans, to engineer AI systems, and the interaction between humans and AI systems. Preferably, this is done by developing, using, or testing formal modelling and simulations of human like behavior, to encourage “understanding by building”. Research in this area can be described as a continuous dialogue between empirical data, psychological modelling and AI engineering.

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AI in Cultural Inquiry and Art

The current ‘Algorithmic Condition’ shapes practices of knowledge and artistic production within cultural inquiry and in creative and cultural professions. As Artificial Intelligence spreads throughout art, culture, and academia, this SIG deepens our understanding of both the specificity of AI and of algorithm-driven creativity in academic and artistic production.

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AI and Behavioural Change: Transitions to a Sustainable Future

Human behaviour is an important root of contemporary societal problems, for instance, overconsumption increasing pollution and deteriorating healthcare. It is therefore crucial to change human behaviour for the better and to maximise global well-being and prosperity. A key component in this endeavour is the use of AI systems, such as smart house heating or cooking prompting us to save energy and adopt novel daily routines. The Special Interest Group on AI and Behavioural Change aims to optimise behavioural change through AI by developing AI systems helping us to make sustainable decisions and act accordingly, as well as to implement these systems in a larger societal context.


AI & Mobility

The energy transition leads to new mobility concepts and vehicle electrification. Changing mobility patterns, e.g. by COVID-19, pose huge challenges. This SIG unites advances in operations research, data mining, and AI, using a human-in-the-loop approach. This is done in an interdisciplinary research cooperation including social, human-behavioral and geographical aspects. As such a multi-disciplinary AI approach is pivotal for some of societal biggest challenges.


Joined Special Interest Groups

Active Learning 

With the emergence of online publishing, the number of scientific papers on any topic is skyrocketing. Simultaneously, the public press and social media also produce data by the second. In this tsunami of new knowledge, imagine updating a medical guideline, making evidence-based policy, or scouting for new technologies: there’s not enough time to read everything. More and more researchers and organisations rely upon Systematic Reviews: attempts to synthesise state-of-the-art in a particular field. The rapidly evolving field of Artificial Intelligence (AI) has allowed the development of AI-aided pipelines that assist in finding relevant texts for search tasks. A well-established approach to increase efficiency is screening prioritisation via active learning: a constant interaction between a human and a machine.

The SIG is a collaboration between the focus area Applied Data Science and Human-centered Artificial Intelligence.