With the proliferation of accessible machine learning tools, there is a pressing need for ethical frameworks within Digital Humanities. Although traditional source criticism is well established, Digital Humanities require a digital source criticism that considers both the historical sources themselves and the data creation process. Often misunderstood as solely gender-focused, Data Feminism provides such a toolkit for addressing bias and ethics. This working paper discusses how these principles originally focused on data science can be adapted to everyday Digital Humanities practice. It provides both theoretical grounding and practical examples, including a case study from our own work, demonstrating the relevance and application of Data Feminist principles for Digital Humanities.
Lang et al. (Thu,) studied this question.