Version 1
: Received: 21 October 2021 / Approved: 22 October 2021 / Online: 22 October 2021 (09:52:36 CEST)
How to cite:
Chauhan, T.; Palivela, H. Study on Significant Drift in the Domain of Explainable Artificial Intelligence. Preprints2021, 2021100324. https://doi.org/10.20944/preprints202110.0324.v1
Chauhan, T.; Palivela, H. Study on Significant Drift in the Domain of Explainable Artificial Intelligence. Preprints 2021, 2021100324. https://doi.org/10.20944/preprints202110.0324.v1
Chauhan, T.; Palivela, H. Study on Significant Drift in the Domain of Explainable Artificial Intelligence. Preprints2021, 2021100324. https://doi.org/10.20944/preprints202110.0324.v1
APA Style
Chauhan, T., & Palivela, H. (2021). Study on Significant Drift in the Domain of Explainable Artificial Intelligence. Preprints. https://doi.org/10.20944/preprints202110.0324.v1
Chicago/Turabian Style
Chauhan, T. and Hemant Palivela. 2021 "Study on Significant Drift in the Domain of Explainable Artificial Intelligence" Preprints. https://doi.org/10.20944/preprints202110.0324.v1
Abstract
Artificial Intelligence (AI) is required since multiple resources are in need to complete depending on a daily basis. As a result, automating routine tasks is an excellent idea. This reduces the foundation's work schedules while also improving efficiency. Furthermore, the business can obtain talented personnel for the business strategy through Artificial Intelligence. Explainability in XAI derives from a combination of strategies that improve machine learning models' environmental flexibility and interpretability. When Artificial Intelligence is trained with a large number of variables to which we apply alterations, the entire processing is turned into a black box model which is in turn difficult to understand. The data for this research's quantitative analysis is gathered from the IEEE, Web of Science, and Scopus databases. This study looked at a variety of fields engaged in the (Explainable Artificial Intelligence) XAI trend, as well as the most commonly employed techniques in domain of XAI, the location from which these studies were conducted, the year-by-year publishing trend, and the most frequently occurring keywords in the abstract. Ultimately, the quantitative review reveals that employing Explainable Artificial Intelligence or XAI methodologies, there is plenty of opportunity for more research in this field.
Keywords
XAI; bibliometric analysis; black box models; artificial intelligence
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
