"Cultural heritage organizations face at least three main challenges. First, the volume of digital archives makes it extremely difficult for archivists to assess records. Applying Artificial Intelligence (AI) and machine learning (ML) to archives is still at an experimental stage, but AI/ ML could become an integral part of archival processes. To manage the sheer bulk and potential sensitivity of records, archivists will also rely on creators to help them make appraisal and selection decisions at the point of deposit. Second, most born-digital collections are currently closed due to a wide range of reasons (including technical issues, copyright, and data protection). Regardless of whether archives are digital or not, archivists need to balance individual rights and the public interest in the context of the General Data Protection Regulation (GDPR) in Europe. Nobody would reasonably claim that all born-digital data should be unlocked and openly accessible. Yet, it is important to recognize that “dark” archives contain vast amounts of data essential to scholars – including email correspondence, drafts of manuscripts, digital photos and videos. Within current legal frameworks, making born-digital archives more accessible is an urgent priority to fully make sense of our cultural heritage. Third, data science and AI are becoming essential tools, but very few scholars (particularly in the humanities) have been trained to master these research methods, a skills gap which in turn has an impact on the training we offer to students." (Introduction, p.7-8)