How Should Digital Humanities Pioneers Manage Their Data Privacy Challenges?

• Published in: Artificial Intelligence for Knowledge Management pp. 75 - 91
• Main Authors: Rousseaux, Francis, Saurel, Pierre
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: IFIP Advances in Information and Communication Technology
• Subjects: Authorship attribution; Big data; Computer music; Digital humanities; Knowledge management; Machine learning; Music information retrieval; Privacy by design; Right management; Style recognition
• ISBN: 3319288679; 9783319288673
• ISSN: 1868-4238; 1868-422X
• DOI: 10.1007/978-3-319-28868-0_5

Since Digital Humanities researchers and developers are regularly creating somehow industrial applications concerning international business, it is time for those communities to be aware and make the most of legacy constraints and opportunities. For instance, let us consider the Computer Music state of the art, and particularly the Music Information Retrieval community and the wonderful algorithms it produces around authorship attribution and style recognition: even if some music style or authorship is finally attributed to some persons, this attribution may not result from a set of computable data somewhere reportable, the information being typically learned (in the sense of Machine Learning, more or less supervised) from dislocated data throughout the big data or the global database, and disseminated in the global programming system. Is this legal? In Europe and worldwide, Privacy by Design (PbD) is the actual response to protect the fundamental right to data protection and to guarantee the free movement of personal data between business stakeholders or Member States.