@inproceedings{c22a1c91c27d479489562e6106997dec,
title = "XAI for learning: Narrowing down the digital divide between {"}new{"} and {"}old{"} experts",
abstract = "Regular eXplainable AI (XAI) approaches are often ineffective in supporting decision-makers across domains. In some instances, it can even lead to automation bias or algorithmic aversion or would simply be ignored as a redundant feature. Based on cognitive psychology literature we outline a strategy for how XAI interface design could be tailored to have a long-lasting educational value. We suggest the features that could support domain-related and technical skills development this way narrowing the digital divide between {"}new{"}and {"}old{"}experts. Lastly, we suggest an intermitted explainability approach that could help to find a balance between seamless and cognitively engaging explanations.",
keywords = "Decision Support Systems, Digital Divide, Expertise Development, Explainability, Machine Learning, Tailored Explanations",
author = "Auste Simkute and Aditi Surana and Ewa Luger and Michael Evans and Rhianne Jones",
note = "Publisher Copyright: {\textcopyright} 2022 Owner/Author.; 12th Nordic Conference on Human-Computer Interaction: Participative Computing for Sustainable Futures, NordiCHI 2022 ; Conference date: 08-10-2022 Through 12-10-2022",
year = "2022",
month = oct,
day = "8",
doi = "10.1145/3547522.3547678",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "1--6",
booktitle = "Participative Computing for Sustainable Futures - Adjunct Proceedings of the 12th Nordic Conference on Human-Computer Interaction, NordiCHI 2022",
}