Trends in Explainable AI (XAI) Literature
This work uses keyword search using the SemanticScholar API and manual curation to collect a well-formatted and reasonably comprehensive set of 5199 XAI papers, to clarify and visualize trends about the size and scope of the literature, citation trends, cross-field trends, and collaboration trends.
Abstract
TheXAIliteratureisdecentralized,bothinterminologyandinpublicationvenues,butrecentyearssawthecommunityconvergearound keywords that make it possible to more reliably discover papers automatically. We use keyword search using the SemanticScholar API and manual curation to collect a well-formatted and reasonably comprehensive set of 5199 XAI papers, available at https: //github.com/alonjacovi/XAI-Scholar. We use this collection to clarify and visualize trends about the size and scope of the literature, citation trends, cross-field trends, and collaboration trends. Overall, XAI is becoming increasingly multidisciplinary, with relative growth in papers belonging to increasingly diverse (non-CS) scientific fields, increasing cross-field collaborative authorship, increasing cross-field citation activity. The collection can additionally be used as a paper discovery engine, by retrieving XAI literature which is cited according to specific constraints (for example, papers that are influential outside of their field, or influential to non-XAI research).