This article examines the transformation of the “Matthew Effect” – a phenomenon of unequal distribution of scientific recognition described by R. Merton – in the context of digitalization and the active integration of artificial intelligence (AI) systems into science. The Matthew Effect conflicts with the norms of “pure science,” also formulated by R. Merton, revealing a gap between ideal principles and actual mechanisms of recognition distribution. An analysis of current research demonstrates that AI algorithms tend to reproduce and amplify existing inequalities, favoring highly cited authors and prestigious journals, while also perpetuating ethnic and geographical disparities. The article argues that the scientific community is capable not only of diagnosing the problem but also of countering it by developing fair recommendation systems, improving scientometric approaches, supporting open noncommercial platforms, and considering regional specificities.
Matthew Effect; Robert Merton; artificial intelligence; algorithmic bias; ethics of science.