This study investigates the impact of gender diversity on the retraction of scientific publications. Analyzing a random sample of one million publications, covering 2,645,304 authors, alongside retraction data from Retraction Watch (39,709 publications), we identify key factors influencing publication retractions. Our findings indicate that mixed-gender teams are more likely to face retractions than all-male or all-female teams, while individual authors are less prone to retractions. Larger research teams have a lower risk of retraction, whereas medium-sized teams (3–10 authors) experience increased risk. A close look at the reasons associated with retractions reveals some notable differences: Male-led publications are often retracted for serious ethical violations, such as data falsification and plagiarism, while female-led publications primarily face procedural errors and updates in rapidly evolving fields. Promoting women to positions of responsibility in mixed collaborations may advance not only gender equity but also the accuracy of the scientific record.

Artificial intelligence (AI) is widely regarded as one of the most promising technologies for advancing science, fostering innovation, and solving global challenges. Recent years have seen a push for teamwork between experts from different fields and AI specialists, but the outcomes of these collaborations have yet to be studied. We focus on approximately 15,000 papers at the intersection of AI and COVID-19—arguably one of the major challenges of recent decades—and show that interdisciplinary collaborations between medical professionals and AI specialists have largely resulted in publications with low visibility and impact. Our findings suggest that impactful research depends less on the overall interdisciplinary of author teams and more on the diversity of knowledge they actually harness in their research. We conclude that team composition significantly influences the successful integration of new computational technologies into science and that obstacles still exist to effective interdisciplinary collaborations in the realm of AI.

Recently, the set of knowledge referred to as “artificial intelligence” (AI) has become a mainstay of scientific research. AI techniques have not only greatly developed within their native areas of development but have also spread in terms of their application to multiple areas of science and technology. We conduct a large-scale analysis of AI in science. The first question we address is the composition of what is commonly labeled AI, and how the various subfields within this domain are linked together. We reconstruct the internal structure of the AI ecosystem through the co-occurrence of AI terms in publications, and we distinguish between 15 different specialties of AI. Furthermore, we investigate the spreading of AI outside its native disciplines. We bring to light the dynamics of the diffusion of AI in the scientific ecosystem and we describe the disciplinary landscape of AI applications. Finally we analyze the role of collaborations for the interdisciplinary spreading of AI. Although the study of science frequently emphasizes the openness of scientific communities, we show that collaborations between those scholars who primarily develop AI and those who apply it are quite rare. Only a small group of researchers can gradually establish bridges between these communities.