Towards interactive and social explainable artificial intelligence for digital history

by Albrecht Richard, Amro Najjar, Igor Tchappi and Joris Hulstijn

Due to recent development and improvements in the field of artificial intelligence (AI), methods of that field are increasingly adopted in various domains, including historical research. However, modern state-of-the-art machine learning (ML) models are black-boxes that lack transparency and interpretability. Therefore, explainable AI (XAI) methods are used to make black-box models more transparent and inspire user trust. Despite numerous opportunities of applying XAI in this field, they have only recently been adopted. Even though calls for incorporating insights from social sciences into the application of XAI exist, applied XAI methods to generate historical insights are static and not user-centric. Furthermore, there exist theoretical frameworks to use XAI methods interactively in order to generate user understanding incrementally, like the social XAI framework.

• URL: TBA

@inproceedings{Albrecht_etal2024,

   author = {Albrecht, Richard and Najjar, Amro and Tchappi, Igor and Hulstijn, Joris},

   title = {Towards interactive and social explainable artificial intelligence for digital history},

  booktitle={Explainable and Transparent AI and Multi-Agent Systems (EXTRAAMAS 2024)},

   year = {2024}

}