Everything is biased … now what?! Introducing the Bias-Aware Framework

In the digital humanities, datasets inherit and perpetuate biases through multiple channels: individual and institutional biases, discriminatory language in archives, unequal representation in collection practices, and algorithmic biases in AI-assisted processing. These biases are compounded throughout the research process, yet the term “bias” itself lacks a clear definition, often causing “bias paralysis.” This paper proposes treating “bias” as a productive category of analysis for digital humanities research through the development of a “Bias-Aware Framework” for dataset creation and contextualisation. It has three components: a Bias Thesaurus creating shared vocabulary across disciplines to address the conceptual instability of “bias” by breaking down this nebulous concept into interrelated issues like representation, gaps, positionality, CARE, etc; a Bias-Aware Dataset Lifecycle Model showing where biases enter the research process; and Guidelines for documenting, describing, and mitigating bias. We approach bias not simply as an error, but as a revealing analytical lens that shapes knowledge production. By explicitly describing these conditions of production, researchers can improve transparency, improve dataset documentation, and enable more informed reuse of their data.