Explainable AI (XAI): Core Ideas, Techniques, and Solutions

506 indexed citations

Abstract

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This paper, published in 2022, received 506 indexed citations. Written by Rudresh Dwivedi, Devam Dave, Omer Rana, Pankesh Patel, Bin Qian, Zhenyu Wen, Tejal Shah, Graham Morgan and Rajiv Ranjan covering the research area of Health Informatics and Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (254 citations), Health Informatics (52 citations) and Control and Systems Engineering (38 citations). Published in ACM Computing Surveys.

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Countries where authors are citing Explainable AI (XAI): Core Ideas, Techniques, and Solutions

Specialization
Citations

This map shows the geographic impact of Explainable AI (XAI): Core Ideas, Techniques, and Solutions. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Explainable AI (XAI): Core Ideas, Techniques, and Solutions with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Explainable AI (XAI): Core Ideas, Techniques, and Solutions more than expected).

Fields of papers citing Explainable AI (XAI): Core Ideas, Techniques, and Solutions

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Explainable AI (XAI): Core Ideas, Techniques, and Solutions. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Explainable AI (XAI): Core Ideas, Techniques, and Solutions.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1145/3561048.

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