Musicology and artificial intelligence

This report provides an overview of Artificial Intelligence in European musicological research, addressed to musicologists, librarians, archivists, curators, educators, research-policy officers, infrastructure coordinators, and tool developers. Moving from conceptual foundations to institutional implications, it maps the affordances and limitations of AI-based models and tools for the discipline, while situating them within the European normative landscape defined by the ALLEA European Code of Conduct for Research Integrity, the EU Artificial Intelligence Act, and the UNESCO Recommendation on the Ethics of Artificial Intelligence. The six complementary sections successively address: (1) methodological foundations, clarifying notions of model, tool, and critical posture, and surveying audio-, image-, encoding-, and language-model-based applications such as automatic music transcription, optical music recognition, score-audio alignment, automated analysis, reconstruction, and LLM prompt­ing; (2) data infrastructure, covering FAIR/CARE governance, ontologies and persistent identifiers, European federated infra­structures, auditory digital twins, and risks of fragmentation, representational bias, quality variability, and copyright; (3) a continuation of the disciplinary stakes involved in source and corpus processing; (4) musicological education in the age of AI, including curricular redesign, AI literacy, and design models such as the Two-Lane Approach; (5) professional practices and responsibilities, examining scholarly credibility, intellectual property, academic integrity frameworks, cultural and creative industry collaborations, institutional and legal risks, gender equality, and lessons from organised labour; and (6) best practic­es, articulating methodological pluralism against technological determinism and formalising principles of ethical reflexivity, human oversight, explainability, reproducibility, and accountability.