Archivist in the machine: paradata for AI-based automation in the archives

Publication Date


Document Type


Publication Title

Archival Science




Recently introduced into the archival sphere, ‘paradata’ is a conceptual framework for defining the character of information resource processing. (Davet J, Hamidzadeh B, Franks P, Bunn J (2022) Tracking the functions of AI as paradata & pursuing archival accountability. In: Archiving 2022: Final Programs and Proceedings, 7-10 June 2022. Society for imaging science and technology, Springfield, VA, USA, pp 83–88) While it need not be applied exclusively to artificial intelligence-based automated systems, paradata can be, should be, and is currently used to explicate the function of AI in the archives. The use of paradata in relation to AI can help ensure that archival ethical principles continue to be honored in an age of increasing automation. This paper summarizes the theoretical background of paradata, documentation created during recent experiments with machine learning-based AI which gestures toward the varieties of paradata already being collected by researchers, and some of the factors which condition paradata’s use. A few currently available data structures for the representation and display of paradata are evaluated, with an eye toward their suitability for different stakeholder groups. Future directions for theoretical and practical research are suggested.

Funding Sponsor

Social Sciences and Humanities Research Council of Canada


Accountability, Artificial intelligence, Automation, Interpretability, Machine learning, Paradata