Towards XAI: structuring the processes of explanations
M. El-Assady, W. Jentner, R. Kehlbeck, U. Schlegel, R. Sevastjanova, F. Sperrle, T. Spinner, D. A. Keim
Proceedings of the ACM Workshop on Human-Centered Machine Learning, Glasgow, UK, 2019Explainable Artificial Intelligence describes a process to reveal the logical propagation of operations that transform a given input to a certain output. In this paper, we investigate the design space of explanation processes based on factors gathered from six research areas, namely, Pedagogy, Storytelling, Argumentation, Programming, Trust-Building, and Gamification. We contribute a conceptual model describing the building blocks of explanation processes, including a comprehensive overview of explanation and verification phases, pathways, mediums, and strategies. We further argue for the importance of studying effective methods of explainable machine learning, and discuss open research challenges and opportunities.
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