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CREATED:20250916T072712Z
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SUMMARY:Ensuring Social Scientific Data Quality and Reproducibility in the Big Data/AI Era: Challenges and Pathways
DESCRIPTION:Salle de séminaire du Bât 4 – Campus Saint Priest\nLIRMM – Prof. Stefan Dietze \nThroughout the last decades\, the social sciences have increasingly adopted novel forms of research data\, e.g. data mined from the web and social media platforms. This together with the recent advances in artificial intelligence (AI) and related areas\, e.g. natural language processing (NLP)\, led to a much more widespread adoption of diverse computational methods\, including techniques from machine learning and\, most prominently\, large language models. However\, increasingly complex computational methods lead to new challenges with respect to transparency\, reproducibility and overall quality of social science research and data\, further elevating an already widely recognised reproducibility crisis. This talk will\, one the one hand\, introduce challenges posed by the use of AI-based methods in social science research. On the other hand\, it will show pathways to address such problems. Examples are works geared towards sharing computational (AI) methods in the social sciences in a reproducible and citable way\, for understanding and tracing adoption of and relations between methods and datasets at large scale\, e.g. in social science research in general (e.g. by mining scientific publications) or novel ways for providing access to sensitive research data in the social sciences (e.g. social media data) to facilitate reproducible research without violating ethical or legal constraints or principles. \nEn savoir plus
URL:https://isdm.umontpellier.fr/event/ensuring-social-scientific-data-quality-and-reproducibility-in-the-big-data-ai-era-challenges-and-pathways/
CATEGORIES:Séminaire
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