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From Access to Ethics: Data Discussions at the DH2025 Conference in Lisbon

by Sofia Baroncini, Constanze Buyken, Judit Garzón Rodríguez, Ian Kisil Marino, Sarah Oberbichler, Cindarella Petz

The 2025 Digital Humanities Conference, held this year in Lisbon under the theme “Building Access and Accessibility: Open Science to All Citizens”, brought together a global community of researchers, developers, and practitioners to reflect on the role of openness, inclusivity, and ethics in digital scholarship. Hosted in the vibrant academic and cultural setting of Lisbon, the conference offered a rich program of keynotes, panels, workshops, and poster sessions, engaging with pressing questions around public participation, digital infrastructure, and the ethical responsibilities of working with data and digital tools in the humanities.

The conference theme built on the legacy of the Budapest Open Access Initiative (2002) and extended the conversation to contemporary challenges and opportunities surrounding Open Science. Contributions addressed a wide range of topics—from citizen humanities and inclusive digital platforms to multilingual practices, the ethics of AI, and FAIR/CARE principles—demonstrating the many ways in which the Digital Humanities community is actively shaping more equitable and transparent research practices. „From Access to Ethics: Data Discussions at the DH2025 Conference in Lisbon“ 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When it comes to analysing large collections of historical documents – like digitized historical newspapers – language models are incredibly powerful tools.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