Research group for AI, ethics and philosophy (AEP)

Research group for AI, ethics and philosophy (AEP)

Research areas/ research interests:

Artificial intelligence is increasingly affecting human and social lives, in diverse domains. As it does so, pressing ethical and political concerns about accountability, responsibility, and power arise. These concerns are connected with, and should be informed by, basic questions about what forms of understanding, intelligence, and communication that AI systems make possible or engender. NTNU’s research group on AI, ethics, and philosophy brings together philosophers, social scientist, and technologists exploring these foundational issues about AI. 

From the point of view of ethics and social studies of technology, topics of concern include the liability to bias in AI systems; their role in facilitating surveillance; how they shift the power balance among private citizens, the state, and major corporations; how they enable large-scale, systematic manipulation and deception. Questions of interest here also include the hopes that have been voiced by some for morally salutary role of AI creating so-called moral machines or fostering moral enhancement.

From the point of view of theories of computation, cognition, and communication, in theoretical computer science, cognitive science, and philosophy, questions explored include what forms of meaning, understanding, or representation that can be attributed to AI systems. This bears on the issue of what sorts of explanation or intelligibility increasingly opaque AI systems may admit of. Notably, it bears upon the extent to which such systems properly can be explained in broadly common-sense or agent-like terms, an issue that must inform what notions of accountability that have application.

Ethics/ applied ethics: Bias in algorithms; Surveillance capitalism; AI/ moral enhancement; Moral machines; Research ethics 

Theoretical-philosophical: Cognition/ philosophy of mind; neuroscienc/conciousness; AI/ language; systems biology; machine learning; AWS 

Empirical, social science: Computerdriven public management; context-dependendence of AI systems; power balance citizen/state


Departments/units:


Research group leaders

Research group leaders

Other members

Other members

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