Hybrid Intelligence – research group at the Department of Informatics
The Hybrid Intelligence research group focuses on advancing our understanding of the synergy between human and machine intelligence, aiming to expand rather than to replace human intellect.
Hybrid Intelligence integrates human expertise and intentionality with machine intelligence to support decision-making and action-taking, while aligning with ethical, legal, and societal values. Our research goal is to study, explore, and design Hybrid Intelligence within the discipline of Information Systems, with a focus on understanding how this transformation affects the role of humans in socio-technical systems, and its implications for keeping humans at the center of this ongoing transformation. We also imbibe the principles of engaged scholarship, fostering close collaboration between researchers in the group and industry partners.
In alignment with our sister group Digital Societies and the department strategy, our vision is to cultivate inclusive and critical knowledge that examines Hybrid Intelligence (HI) at individual, organisational, and societal levels. To achieve this, we draw on a diverse range of theories, methods, and approaches, integrating insights from information systems, computer science, interaction design, and digital innovation. By combining technological and human-centered perspectives, we strive to develop a more holistic understanding of hybrid intelligence and its future impact.
Digital Societies – research group at the Department of Informatics
Research strategy for the Department of Informatics
Research areas
- Human-AI collaboration: Human-AI collaboration amplifies strengths by combining human creativity and critical thinking with AI's speed, scalability, and data-driven insights.
- AI in organisation and society: Studying the impact of AI in organisation and society including, AI literacy, generative AI and equitable access.
- Generative AI in teaching: Generative AI is reshaping education through personalised learning, automated feedback, and course material development. While promising, its integration also presents ethical and pedagogical challenges, requiring thoughtful strategies for effective use.
- Generative AI in research: Generative AI is transforming the way researchers interact with data, offering unprecedented opportunities to engage with both qualitative and quantitative datasets in dynamic and intuitive ways.
- Business analytics and machine learning: With machine learning driving technological advancements, this area explores the latest research, real-world applications, and cross-disciplinary innovations shaping the future of business analytics and machine learning.
- Business Decision Management (BDM) and Automated Decision Making (ADM): Focuses on automating, optimising, and governing business decisions through data-driven insights, rule-based systems, and AI to enhance efficiency and agility in organisational processes.