The "Collections as ML Data" Checklist for Machine Learning & Cultural Heritage

TL;DR

This paper introduces the 'Collections as ML Data' checklist, a set of guidelines designed to assist practitioners in responsibly developing machine learning projects with cultural heritage data, emphasizing sociotechnical considerations.

Contribution

It formulates a detailed checklist with guiding questions for responsible ML project development in cultural heritage, filling a gap in existing organizational-level guidelines.

Findings

The checklist is justified through survey of existing projects.

Guiding questions operationalize responsible ML practices.

The checklist can be published with project deliverables.

Abstract

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.