Should the novel scientific findings generated by AI-powered systems like AlphaFold be considered as instances of machine creativity?
When does human-AI collaboration lead to successful scientific problem solving, and when does it fail?
Are AI systems reconfiguring the scientific method?
Currently we cannot answer such questions, because systematic empirical studies of problem-solving activities in AI-augmented labs have not been conducted. Nor do we have the conceptual tools needed to evaluate the rationality of problem solving in AI-augmented labs. That is the gap the SCI-AI project addresses. The main aim of the project is to generate understanding of the AI-augmented discovery transformation and its consequences for the development of human knowledge. The main objectives are:
(A) to uncover and analyze problem-solving processes in AI-augmented labs,
(B) to examine the implications of AI-augmented discovery for the epistemology of science, and
(C) to use automated discovery as a mirror for exploring human creativity and intellect.
Computational models of problem-solving open a view into the key steps of the processes underlying scientific discovery. Our models combine the tradition of models of heuristic problem-solving in cognitive science with the empirical data collected in SCI-AI by using cognitive ethnography. The goal of modeling is to explain the functioning of the cognitive resources and operations that researchers utilizing AI-systems contribute to the problem-solving processes. Our models can be implemented as computer programs to explore the functioning of problem-solving across a variety of scenarios.
When using cognitive ethnography to study a research lab, the ethnographer spends time in the lab to observe the different stages of scientific problem-solving. Through interviews with lab members, protocol analysis, and participant observation, the ethnographer collects rich qualitative data on the key stages of the AI-augmented problem-solving processes.