AI Research Needs New Rules to Deliver Real-World Impact, Not Just Citations
A new paper argues that AI research across industries is stuck optimizing for academic prestige rather than solving actual problems. Researchers propose shifting from competition-based metrics to collaborative frameworks that measure societal impact—a change that could reshape how companies and institutions fund and evaluate AI initiatives.
Originaltitel: On the importance of AI research beyond disciplines: establishing guidelines
<p>Artificial intelligence (AI) has evolved into a prominent player in various academic disciplines, transforming research approaches and knowledge generation. This paper explores the growing influence of AI across diverse fields and advocates for meaningful interdisciplinary AI research. It introduces the concept of "agonistic-antagonistic" interdisciplinary research, emphasizing a departure from conventional bridge-building approaches. Motivated by the need to address complex societal challenges, the paper calls for novel evaluation mechanisms that prioritize societal impact over traditional academic metrics. It stresses the importance of collaboration, challenging current systems that prioritize competition and individual excellence. The paper offers guiding principles for creating collaborative and co-productive interdisciplinary AI research environments, welcoming researchers to engage in discussions and contribute to the future of interdisciplinary AI research.</p>