Interpretable Outputs: Criteria for Machine Learning in the Humanities
James E. Dobson · Digital humanities quarterly · 2020
There are a number of risks to interpretability found in the growing use of complicated computational methods within the humanities. Greater attention needs to be given to the format and presentation of the underlying data and the interpretation of any data-derived results. Visually appealing graphic renderings of data, high classification accuracy and confidence scores, and impressive summary statistics can be rhetorically structured to appeal to preconceived notions and commonplace understandings. While making arguments from the summary of significant features might be permissible in some applications of what is now commonly called data science, humanistic uses of computation put forward in the service of academic arguments require much greater access to and grounding in interpretable digital objects. When features are derived from text, these underlying features need to be shown and interpreted. This essay argues that claims made on behalf of computational models need to be evaluated and warranted by a set of shared assumptions and the ability to test and verify that the data are indeed comprehensible according to the norms of the shared interpretative community.