Explainable Artificial Intelligence: Current Trends and Future Directions Using Bibliometric Analysis
SSRN, ISSN: 1556-5068
2024
- 117Usage
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
This paper outlines the primary topics in the field of explainable artificial intelligence as well as the present dynamics of the subject and offers future research options. The paper evaluates a sample of 3214 papers from the Scopus database using a bibliometric approach in order to discover research activity on explainable artificial intelligence that took place between the years 1974 and the present date, including the early access of 2024, which is half a century. For holistic understanding, the paper provides answers to five important research questions that will help in determining which papers and writers have the greatest influence based on how important they are to it. In addition, it also looks at the most recent trends and identifies themes related to future research related to explainable artificial intelligence. As explainable artificial intelligence is becoming relevant for research in many research areas, the paper will help researchers in a holistic understanding of explainable artificial intelligence research. The paper fulfils an important need of exploring and analysing the research related to explainable artificial intelligence.
Bibliographic Details
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