On the Cruelty of Really Writing a History of Machine Learning
Aaron Plasek · IEEE Annals of the History of Computing · 2016
The construction, maintenance, and mobilization of data used to both constrain and enable machine learning systems poses profound historiographical questions and offers an intellectual opportunity to engage in fundamental questions about novelty in historical narratives. To effectively explore the intellectual, material, and disciplinary contingencies surrounding both the curation and subsequent distribution of datasets, we need to take seriously the field of machine learning as a worthy subject for historical investigation.