The “Collections as ML Data” checklist for machine learning and cultural heritage
针对文化遗产领域应用机器学习时缺乏研究者指南的问题，本文制定了一份详细清单，包含指导性问题与实践，帮助研究者负责任地开展项目，并可通过发布清单增强透明度。
Abstract Within cultural heritage, there has been a growing and concerted effort to consider a critical sociotechnical lens when applying machine learning techniques to digital collections. Though the cultural heritage community has collectively developed an emerging body of work detailing responsible operations for machine learning in galleries, museums, archives, and libraries at the organizational level, there remains a paucity of guidelines created for researchers embarking on machine learning projects with digital collections. The manifold stakes and sensitivities involved in applying machine learning to cultural heritage underscore the importance of developing such guidelines. This article contributes to this need by formulating a detailed checklist with guiding questions and practices that can be employed while developing a machine learning project that utilizes cultural heritage data. I call the resulting checklist the “Collections as ML Data” checklist, which, when completed, can be published with the deliverables of the project. By surveying existing projects, including my own project, Newspaper Navigator, I justify the “Collections as ML Data” checklist and demonstrate how the formulated guiding questions can be employed by researchers.