On the Historiographic Authority of Machine Learning Systems

The integration of Machine Learning in historical research has significantly altered the approach to sources, data and workflows. Historians now use Machine Learning applications such as Handwritten Text Recognition (HTR) and Natural Language Processing (NLP) to manage large corpora, enhancing research capabilities but also introducing challenges in combining machine-generated and manually created data without propagating errors. The reliability of machine-generated data is a central concern, paralleling issues found in traditional transcription and edition practices. The concept of factoids highlights the fragmentation and recontextualization of data in digital history. Evaluating Machine Learning systems, particularly through tools like CERberus for HTR, emphasises the need for qualitative error analysis to support historical research. The article proposes three strategic directions for digital history: defining clear needs to manage data pragmatically, enhancing transparency to improve data reuse and interoperability, and advancing data criticism and hermeneutics. These directions aim to refine the methods and practices of digital historians, ensuring that Machine Learning outputs are critically assessed and effectively integrated into historical scholarship.