REEVALUATE Project Position Paper - AI Strategy for the Cultural and Creative Sectors

This position paper presents the REEVALUATE Horizon Europe project's contribution to the European Commission's Call for Evidence on the AI Strategy for the Cultural and Creative Sectors (September 2026). Drawing on REEVALUATE's technological developments, integrated platform, and pilot experiences in fashion, Olympic heritage and music, and archaeology, the paper provides policy recommendations for responsible and sustainable AI adoption across the cultural and creative sectors. The recommendations address four key areas: Innovation and collaboration: Domain-adaptable AI, semantic interoperability, co-design with cultural professionals, and meaningful human oversight. Fair business models, ethics, and rights protection: Machine-readable rights and provenance, reciprocal access to cultural data, and safeguards for contextual integrity. Skills and sustainability: Institutional AI literacy, capacity building, organizational adaptation, and long-term maintenance of digital cultural infrastructures. EU policy priorities: Coordinated support for domain adaptation, interoperability, human agency, rights management, responsible reuse, and sustainable cultural AI ecosystems. The paper emphasizes that effective cultural AI requires more than technological capabilities. It depends on trusted cultural data, domain expertise, human judgment, interoperable infrastructures, and appropriate safeguards for the responsible use and reuse of cultural heritage. Author: Dimitrios Grigoriadis, Hypertech Sustainability Research & Technology (HSRT). Prepared on behalf of: The REEVALUATE Consortium. Project: REEVALUATE – Horizon Europe, Grant Agreement No. 101132389. Original submission: European Commission, Have Your Say – AI Strategy for the Cultural and Creative Sectors, 11 September 2026. Official submission: https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/18592-Artificial-intelligence-strategy-for-the-cultural-and-creative-sectors/F33572477_en This work was developed within the REEVALUATE project, funded by the European Union's Horizon Europe Research and Innovation Programme under Grant Agreement No. 101132389.