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Published in: KI - Künstliche Intelligenz 1/2023

Open Access 22-05-2023 | Editorial

Avoid Predatory Journals

Author: Daniel Sonntag

Published in: KI - Künstliche Intelligenz | Issue 1/2023

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Notes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
In the Article “Predatory Journals: No Definition, no defence” (Springer Nature, Vol 576, Dec 2019), the Authors Argue that Predatory Journals are a Global Threat. I Agree with that. Without Performing Checks for Scientific Quality and Issues such as Plagiarism, it is easy to Scale the Number of Accepted Articles or even Special Issues; the Resulting Business Model is Based on Establishing open Access broad-discipline Journals, with Article Processing Charges paid by the Authors. In These Journals, Editorial and Academic Rigor is Neglected in Favor of Operational Speed and Business Interests. Interestingly, Publication fees of Predatory Journals are Ultimately paid out by the Funding Agencies such as DFG and BMBF (in Germany).
The Purpose of this text is to Motivate the German AI Community to Ensure that our German Journal of Artificial Intelligence from the GI can keep up its Rigorous Reviewing Process and will Never be Subject to such a Development. We will Hence cross-check our Journal’s Characteristics. Scientific Publishers (Including Springer Nature) have Helped to Establish Guidelines for Editorial Board Members and Authors.
One of these guidelines says that negligent peer review is a prominent feature of predatory journals. Here, the call is on you. You are asked to do something, to help ensure that we get enough quality reviews for our submission. Delayed processing times are most often a result of the fact that it takes time to get at least three meaningful and expressive reviews per submitted article because many colleagues reject to review articles on a regular basis. The acceptance rate, based on the scientific review process, is currently at about 30%. Luckily, Springer Nature supports us in this process with desk reject information such as automatic plagiarism check results and concurrent submission information to ease the task. However, we need your voluntary service, dear experts of AI in Germany, in the same way as the readers and the AI community need voluntary editorial board members for scientific, non-profit publishing. Springer Nature and the GI give us something back which is very precious: if you are a corresponding author affiliated with a German university or research institution, you are entitled to publish open access in our KI Journal with fees covered by the German DEAL agreement.
Another predatory journal guideline highlights the lack of transparency. This concerns transparency in operational procedures (e.g., how editorial decisions are made, publication fees applied, and the organisation of peer reviews), covered by the previous paragraph. A second point concerns contact information or details about article processing charges (cf. DEAL). Predatory journals and publishers often make guest editors and members of their editorial boards unverifiable. In contrast, you can visit our webpage (https://​www.​springer.​com/​13218) to see the KI Journal’s information in a transparent way. A third point about transparency is important to mention: Springer announced “Online First” in 2014, all final articles are published online after an author has reviewed proofs and all corrections have been carried out. They are in citable form 2–3 weeks after acceptance and before distribution of the print journal as a special issue. Metadata is sent to all relevant bibliographic services for inclusion in indexing databases immediately after online publication; here DBLP is very relevant for the AI community. Over the last 10 years, we have established a stable publication schedule of 12–14 accepted articles per issue (please note the indicator for predatory journals, that it is easy to scale the number of accepted articles, as mentioned above, to be in stark contrast to this).
Downloads per geography in 2012 include North America (9%), Asia Pacific (31%), and Europe (56%) compared to downloads per geography in 2023 of North America (16%), Asia Pacific (28%), and Europe (47%). Successful full-text downloads are a very promising development: 8797 (2011), 9894 (2012), 16,887 (2013), …, 92,019 (2020), 114,628 (2021), and 134,858 in 2022. The h5 Index was 13 in 2014 and is 23 in 2023. Most papers have been submitted and accepted by Germany, USA and Denmark, followed by Austria, France, Japan and the UK, making up 85%.
I hope I have motivated to publish with us. The authors in “Predatory journals: no definition, no defence” argue that it can be tough to distinguish a predatory journal from a journal that is under-resourced. I would like to repeat my main point. We need your voluntary service, dear experts of AI in Germany, in the same way as the readers need voluntary editorial board members for scientific, non-profit publishing, which requires collaboration and support. The service of the editorial board members, the guest editors, the reviewers and German AI community and members has already achieved the following: the Scimago Journal Rank (SJR), which is based on Elsevier’s Scopus database, is generated by an independent agency, calculating the number of citations in one year to a journal’s articles in the preceding three years; the KI Journal is in the second quartile (heading towards top quartile). Other academic publication incentives include an impact score of 1,91 and a CiteScore of about 4, matching AI Magazine (referring to the magazine character of our Journal with AI market, interviews, discussions, news, dissertation abstracts and project sections). A cite score of about 4 is also achieved by AI and Society or the Journal of Robotics and Control, emphasising the technical articles’ strength. A recent development is that the KI Journal has been indexed by Clarivate’s Web of Science and the journal’s impact factor (JIF) is being calculated.
Best regards,
Daniel Sonntag

1 Forthcoming Special Issues

1.1 AI in Current and Future Agriculture

Special Issue Guest Editors: Joachim Hertzberg (University of Osnabrück, DFKI), Jan Christoph Krause (DFKI), Benjamin Kisliuk (DFKI).
Agriculture is a perfect field for applying AI technology: uncertainty, data-rich and knowledge-rich applications and a high degree of digitalization in today’s farming technology. Today, assistive technologies as seen in precision agriculture, farm management systems and monitoring systems improve existing processes and improve their performance, while various robots have been in use in animal husbandry and start getting used in crop farming.
Still, there is a lack of fully automated and integrated solutions for conventional agriculture which would transform practical procedures. Further, alternative cultivation concepts like agroforestry, spot farming and mixed cultivation approaches could become feasible by AI in the first place.
For allowing AI to enable this transformation in agriculture, advances would be required in the fields of perception, navigation, autonomy, learning, data analysis, inference and (multi) robot control. Besides improving the technology, compliance with ethical, legal and social implications is vital for putting AI further into practice as well as to increase acceptance of users as of society at large.
This Special Issue aims at providing an overview of work in AI in agriculture regarding, but not limited to, the following topics. All submissions will be peer- reviewed:
  • Monitoring and data acquisition in agricultural applications.
  • AI-based assistive systems for decision making and execution.
  • Robotic solutions for automating (partial) processes.
  • Upcoming developments of robotic and AI technologies in agriculture.
  • AI for alternative agriculture concepts.
  • AI for indoor farming.
  • Human-robot-interaction and user acceptance.
  • ELSI aspects of AI in agriculture.

1.2 AI in Healthcare & the Public Sector

Special Issue Guest Editors: Tanya Braun (University of Münster), Ralf Möller.
(University of Lübeck, DFKI)
Healthcare and the public sector are fields that provide many interesting challenges as an application area for AI technologies: there is a plethora of data in some cases and only a handful of data points in others. Data is noisy, influences are uncertain, and causal interactions at times not fully understood. The scenarios under investigation can become very large with multiple players with diverse interests coming together. Researchers therefore deal with a rich set of different problems. Consequently, solutions are based on a wide array of techniques. In addition, human-centered and explainable AI are of particular importance when providing automated data analysis services or decision support. Ethical and privacy considerations also play a large role in healthcare applications and applications in the public sector as they possibly deal with very personal information. For enabling AI to provide a rich set of helpful services, advances are necessary that not only improve technologies but also consider ethical, legal, and social implications and increase acceptance in society at large.
This special issue aims at providing an overview of work in AI in healthcare and the public sector regarding, but not limited to, the following topics:
  • Data analysis techniques including dealing with heterogeneous data sources or data streams.
  • Decision support.
  • Robotics in healthcare.
  • Human-centered AI, human-computer interaction.
  • Trustworthy AI, explainability, transparency.
  • Ethical, legal, and social implications of AI.
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creativecommons.​org/​licenses/​by/​4.​0/​.

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KI - Künstliche Intelligenz

The Scientific journal "KI – Künstliche Intelligenz" is the official journal of the division for artificial intelligence within the "Gesellschaft für Informatik e.V." (GI) – the German Informatics Society - with constributions from troughout the field of artificial intelligence.

Metadata
Title
Avoid Predatory Journals
Author
Daniel Sonntag
Publication date
22-05-2023
Publisher
Springer Berlin Heidelberg
Published in
KI - Künstliche Intelligenz / Issue 1/2023
Print ISSN: 0933-1875
Electronic ISSN: 1610-1987
DOI
https://doi.org/10.1007/s13218-023-00805-w

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