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Anne Henriksen

PhD Student

Anne Henriksen


AI intelligent systems, knowledge production, and trustworthy partnerships

Intelligent systems have long been discussed as a possible application of Artificial Intelligence (AI) to augment, supplement, or substitute for human knowledge and the brain. With the recent years’ technological advancement of computing power and Machine Learning and the growth of Big Data, such applications are in earnest becoming a reality through AI models. We see increasingly more examples of systems deployed in various work processes to assist or drive for instance employment of the most ‘right’ employee, facilitation of the most ‘right’ student learning, or provision of the most ‘right’ patient treatment.


How are intelligent systems designed to contribute with reliable knowledge in order to decide such matters which often not are clear-cut right or wrong in the specific context or across competing knowledge communities and paradigms? What kind of partnership and delegation of competency and knowledge are thus created in the human-machine collaboration produced? And what are the critical aspects of optimizing knowledge, decision and work practice through Big Data and AI?


This PhD project is interested in these questions and more specifically in AI-models as a knowledge production and decision-making technology. Through a deep dive into two specific cases, it studies the sociotechnical practice by which AI-models are applied to semi-automatically produce the probabilistic knowledge that underlies AI decision assistance and AI decision performance in two different and more or less critical domains: healthcare and accountancy. Furthermore, the project discusses the assumptions that this kind of knowledge production bases on, what the contribution of AI-models is from a knowledge perspective, and to what extent we can count on that AI-models in any case will lead to more clear-slightness, better decisions and more knowledgeable professionals, organizations, and societies.


The project bases on a qualitative case study in an AI company and uses an ethnographic inspired methodological approach along with a work process and practice-oriented focus. It contributes to the existing research literature on AI sociotechnical systems in work, organizations, and society by producing new knowledge and understandings of knowledge production with AI and human-computer collaboration.


Funding partners: Faculty of ARTS, Aarhus University

Supervisors: Anja Bechmann & Finn Olesen


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