Within current debates about the future impact of Artificial Intelligence on human society, roughly three different perspectives can be recognised: the technology-centric perspective, claiming that AI will soon outperform humankind in all areas, and that the primary threat for humankind is superintelligence; the human-centric perspective, claiming that humans will always remain superior to AI when it comes to social and societal aspects, and that the main threat of AI is that humankind’s social nature is overlooked in technological designs; and the (...) collective intelligence-centric perspective, claiming that true intelligence lies in the collective of intelligent agents, both human and artificial, and that the main threat for humankind is that technological designs create problems at the collective, systemic level that are hard to oversee and control. The current paper offers the following contributions: a clear description for each of the three perspectives, along with their history and background; an analysis and interpretation of current applications of AI in human society according to each of the three perspectives, thereby disentangling miscommunication in the debate concerning threats of AI; and a new integrated and comprehensive research design framework that addresses all aspects of the above three perspectives, and includes principles that support developers to reflect and anticipate upon potential effects of AI in society. (shrink)

Research on expert‐novice differences has mainly focused on how experts solve familiar problems. We know far less about the skills and knowledge used by experts when they are confronted with novel problems within their area of expertise. This article discusses a study in which verbal protocols were taken from subjects of various expertise designing an experiment in an area with which they were unfamiliar. The results showed that even when domain knowledge is lacking, experts solve a novel problem within their (...) area of expertise by dividing the problem into a number of subproblems that are solved in a specified order. The lack of domain knowledge is compensated for by using abstract knowledge structures and domain‐specific heuristic strategies. However, the quality of their solutions is considerably lower than the quality attained by experts who were familiar with the type of problem to be solved. The results suggest that when experts are confronted with novel problems as compared with familiar problems, their form of reasoning remains intact, but the content of their reasoning suffers due to lack of domain knowledge. (shrink)