Why Looking More Closely at Our Data Is the Way to Better Research Ethics

Better data leads to better research. That might sound obvious, but in practice, it’s often not where the attention goes. In conversations about AI, digital humanities, or computational research, the spotlight usually lands on models—new tools, new methods, new technical breakthroughs. Meanwhile, the data those models rely on quietly sits in the background, treated as a given. It isn’t. We know that from the data/capta discourse. If we actually care about research ethics, we need to shift that focus. Because most of the ethical issues people worry about don’t start with the model. They start with the data. And data, especially in historical research, is messy. It’s incomplete, shaped by power structures, and full of gaps. Some voices were never recorded. Others were preserved unevenly. So when we build datasets, we’re not just collecting neutral material, we’re making choices: What gets included? What gets left out? What gets cleaned up, standardised, or ignored? This is where things get interesting—and where

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