  Within the cultural heritage sector, 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 libraries and other cultural
heritage institutions at the organizational level, there remains a paucity of
guidelines created specifically for practitioners embarking on machine learning
projects. The manifold stakes and sensitivities involved in applying machine
learning to cultural heritage underscore the importance of developing such
guidelines. This paper 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 and operationalized.
