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Data ethics in historical research: Recognizing responsibility

Data may be misleadingly considered an objective and impartial product. When doing historical research based on data (re)use, applying an ethical perspective becomes essential. Data is neither neutral or objective, nor are our practices for accessing, analyzing, and documenting it. Any discourse on the production of knowledge or on technology that creates and/or manipulates knowledge implies an ethical discourse and (un)conscious decisions. Ethics imply responsibility, as ignoring ethical issues or not taking action means, in many cases, perpetuating the existing biases and inequalities. The need for scholarly exchange in order to navigate the multifaceted and complex dimensions of research ethics was also reflected in the more than 200 registrations for the conference “Data Ethics for Historical Research in a Digital Era”, about which we would like to share our thoughts in this blog post.1

„Data ethics in historical research: Recognizing responsibility“ weiterlesen

  1. Author contribution according to CReditT: Writing – original draft: Sofia Baroncini, Constanze Buyken, Fabian Cremer, Ian Kisil Marino, Sarah Oberbichler, Cindarella Petz, Elena Suárez Cronauer, Thorsten Wübbena. []

From “so what?” to “what’s next?”: Joining the C²DH in Luxembourg as Visiting Researcher

by Sarah Oberbichler

There are critics who sometimes ask the “so what?” question when it comes to Digital History: what impact can these new methods and approaches make on historical arguments and how can they advance scholarship? Do they advance scholarship at all? To be fair, sometimes this critique is justified.

The “so what?” question  has long been part of the C²DH Centre’s critical approach to digital history.

I have always admired how the C²DH Centre in Luxembourg approaches this very question. Rather than dismissing the critique, they’ve made it central to their philosophy. What makes the Centre so compelling is how they address it: they recognize that digital historians are foremost historians who face the same fundamental challenges—working with complex, incomplete, messy sources full of biases and errors, then interpreting them critically to develop arguments. But digital sources add new layers of complexity: digitization processes, algorithmic biases, complex tools. This is where new skills need to be learned (digital literacy and competence), where collaboration becomes essential, and where experimentation is key.

The C²DH Centre is a place where failure is part of success.

„From “so what?” to “what’s next?”: Joining the C²DH in Luxembourg as Visiting Researcher“ weiterlesen

LLM Biases: Expected and Unexpected Model Design Effects in Historical Newspaper Article Extraction on the Messina Earthquake

by Johanna Mauermann and Sarah Oberbichler

When it comes to analysing large collections of historical documents – like digitized historical newspapers – language models are incredibly powerful tools. But as these technologies become more common in humanities research, scholars also need to think critically about how they use them.

One big question researchers face when using language models for their analysis is about bias. Described by Rob Kitchin in his book „Critical Data Science,“ bias can be understood as „a consistent pattern of error within a dataset, or within a method of data processing and analysis, that skews findings and interpretation“ (Kitchin, 2024). Biases manifest in AI models on various levels: in the model design, in the data itself, in its application to contexts the model wasn’t trained for, and in the prompts themselves (compare e.g., Ferrer et al., 2021). Addressing bias questions therefore is crucial for ensuring our historical research stands up to scrutiny and Good Scientific Practice demands that we take these challenges seriously. „LLM Biases: Expected and Unexpected Model Design Effects in Historical Newspaper Article Extraction on the Messina Earthquake“ weiterlesen

Large-Scale Research with Historical Newspapers: A Turning Point through Generative AI

by Sarah Oberbichler

Digitized historical newspapers have been a crucial source for my research over the past decade. Exactly ten years ago, in 2014, I began my research journey with historical newspapers at the University of Innsbruck in Austria. Using a semi-automated multilingual approach to analyze a corpus of over 20,000 articles from German and Italian newspapers, I also started learning how to apply digital methods to historical sources: analyzing large-scale historical newspaper archives simply required such skills. „Large-Scale Research with Historical Newspapers: A Turning Point through Generative AI“ weiterlesen