The growing accessibility of cultural heritage data, together with the large‑scale digitization of collections, has transformed how heritage materials are discovered and used. While this expansion offers clear benefits, it also introduces new challenges: collections are often published online without sufficient curatorial oversight, which can unintentionally reinforce discriminatory narratives and hinder genuine inclusion in cultural and digital spaces. This underscores the need for heritage institutions to ensure that metadata is responsibly curated or at least that potential issues are clearly identified and flagged. In response to this, digital technologies and AI are currently being explored for the identification and flagging of potentially biased or harmful metadata in large‑scale digital collections.
These issues were recently addressed in the context of DE-BIAS, a two-year project (2023–2025) co-funded under the Digital Europe Programme (DIGITAL) of the European Union and grounded on the collaboration between 11 European GLAM institutions. The project developed co‑creative, technology‑driven strategies to support the contextualization and identification of bias in heritage collections, and to encourage cultural heritage institutions to describe digital collections more respectfully and inclusively. It implemented a capacity‑building strategy that combined co‑creation methodologies in workshops and events with tools for co-detecting bias in heritage collections. The project also developed an AI‑powered tool which has been integrated into the Europeana portal, that, by leveraging a multilingual vocabulary -also produced within the project- allows to automatically flag and provide contextual information about potentially contentious terminology in heritage metadata.
Building on the outcomes of the DE‑BIAS project, this study investigates how explainable AI and data‑visualization techniques can be adopted to support GLAM institutions in systematically detecting issues within their collections’ metadata. By using as a case study photographic collections from the Wellcome Collection and Photoconsortium, this research draws on the iCANDID infrastructure to analyse collections metadata and to inform the development of a diagnostic dashboard designed to assess collections’ metadata quality and identify areas requiring remediation. The objective is to provide cultural heritage institutions with tools that support systematic evaluation and diagnosing of their collections by transforming metadata into visual structures that highlight discrepancies or imbalances in the descriptive patterns. This process aims at facilitating the interpretation and identification of potential biases or descriptive imbalances, such as limited richness, incomplete records, multilingual inconsistencies, and representational gaps.