Dear corpora-list,
We would like to draw your attention to the Thematic Track "AI in Digital Humanities, Computational Social Sciences and Economics Research (AI-HuSo)", which will take place at FedCSIS 2024 in Belgrade from September 8-11, 2024. Submission deadline is on *May 28th 2024*.
The event aims to bring together research from various disciplines in the humanities, social sciences and economics, focusing on the use of computational methods, machine learning and AI.
Further information on the topics and the deadlines can be found here: https://2024.fedcsis.org/thematic/ai-huso Contact: ai-huso@fedcsis.org
======================================================================= AI in Digital Humanities, Computational Social Sciences and Economics Research (AI‑HuSo) ======================================================================= Belgrade, Serbia, 8–11 September, 2024
This thematic track is dedicated to the computational study of Social Sciences, Economics and Humanities, including all subjects like, for example, education, labour market, history, religious studies, theology, cultural heritage, and informative predictions for decision-making and behavioural-science perspectives. While digital methods and AI have been emerging topics in these fields for several decades, this thematic track is not only limited to discoveries in these domains, but also dedicated to the reflections of these methods and results within the field of computer science. Thus, we are in particular interested in interdisciplinary exchange and dissemination with a clear focus on computational and AI methods.
Since there is a clear methodological overlap between these three domains and often similar algorithms and AI approaches are considered, we see this thematic track as place for interdisciplinary learning, discussing a joint toolbox as a support for scholars from these field with human and context-aware agents.
The aim of this thematic track is thus to bridge the gap between scientific domains, foster interdisciplinary exchange and discuss how research questions from other domains challenge current computer science. In particular, we are interested in communications between researchers from different fields of computer science, social sciences, economics, humanities, and practitioners from different fields.
Topics The list of topics includes, but is not limited to:
- AI and computational approaches for the interdisciplinary work of the social sciences, economics, and humanities: report on theoretical, methodological, experimental, and applied research. - AI and computational approaches for linking data from different digital resources, including online social networks, web and data mining, Knowledge Graphs, Ontologies. - AI and computational methods for text mining and textual analysis, for example texts within social sciences, digital literary studies, computational stylistics and stylometry. - Text encoding, computational linguistics, annotation guidelines, OCR for humanities, economics, and social sciences. - Network analysis, including social and historical network analysis.
While we encourage submissions from a broad background, every year we also encourage submissions to two special topics. In 2024 these will be:
- Ethical and philosophical considerations of AI in society and research. - Sociological challenges for AI in society, e.g., labour market, education or media.
In general, the applications of interest are included in the list below, but are not limited to:
- Labour market research and qualification, including behavioral-science perspectives. - Education: Digital methods and systems, e-learning, adult education, etc. - Contributions to the application of technology to culture, history, and societal issues: For example, computational text analysis, analytical and visualization, databases, etc. - In particular, we welcome submissions which focus on a critical reflection of digital methods in the humanities, economics and social sciences within computer science. - Linking of digital resources, a discussion of data sets, their quality and reliability, combining quantitative and qualitative data, anonymization and data protection.