This dissertation introduces three primary contributions through publicly deployed sys- tems and datasets. First, we demonstrate how the construction of large-scale cultural heritage datasets using machine learning can answer interdisciplinary questions in library & information science and the humanities (Chapter 2). Second, based on the feedback of users of these cultural heritage datasets, we introduce open faceted search, an extension of faceted search that leverages human-AI interaction affordances to empower users to define their own facets in an open domain fashion (Chapter 3). Third, encountering similar challenges with the deluge of scientific papers, we explore the question of how to improve recommender systems through human-AI interaction and tackle the broad challenge of advice taking for opaque machine learners (Chapter 4).
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |
