So Everything is Biased… Now What?!

As the Digital Humanities increasingly rely on computational methods—from handwritten text recognition to automated metadata extraction—addressing bias becomes both more urgent and more complex. This paper introduces the Bias-Aware Framework, a research framework that transforms bias from a paralyzing problem into an analytical category with clear action points. The framework consists of three key components: (1) a Bias Thesaurus (2) a Data Lifecycle Model, and (3) Guidelines for documenting, describing, and mitigating bias.