Open Data and transparency in artificial intelligence and machine learning: A new era of research version 1; peer review: not peer reviewed
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F1000Research 2023, 12:387 Last updated: 27 NOV 2023
EDITORIAL
Open Data and transparency in artificial intelligence and
machine learning: A new era of research [version 1; peer
review: not peer reviewed]
Caellin M. Rodgers 1, Sally R. Ellingson2, Parag Chatterjee 3,4
1F1000, London, UK
2UK College of Medicine, Markey Cancer Center, Lexington, Kentucky, USA
3Universidad Tecnologica Nacional Facultad Regional Buenos Aires, Buenos Aires, Autonomous City of Buenos Aires, Argentina
4Universidad de la Republica Uruguay, Montevideo, Montevideo Department, Uruguay
v1 First published: 12 Apr 2023, 12:387 Not Peer Reviewed
https://doi.org/10.12688/f1000research.133019.1
Latest published: 12 Apr 2023, 12:387
https://doi.org/10.12688/f1000research.133019.1 This article is an Editorial and has not been
subject to external peer review.
Abstract Any comments on the article can be found at the
Artificial Intelligence (AI) and machine learning are the current end of the article.
forefront of computer science and technology. AI and related sub-
disciplines, including machine learning, are essential technologies
which have enabled the widespread use of smart technology, such as
smart phones, smart home appliances and even electric toothbrushes.
It is AI that allows the devices used day-to-day across people’s
personal lives, working lives and in industry to better anticipate and
respond to our needs. However, the use of AI technology comes with
a range of ethical questions – including issues around privacy,
security, reliability, copyright/plagiarism and whether AI is capable of
independent, conscious thought. We have seen several issues related
to racial and sexual bias in AI in the recent times, putting the reliability
of AI in question. Many of these issues have been brought to the
forefront of cultural awareness in late 2022, early 2023, with the rise
of AI art programs (and the copyright issues arising from the deep-
learning methods employed to train this AI), and the popularity of
ChatGPT alongside its ability to be used to mimic human output,
particularly in regard to academic work. In critical areas like
healthcare, the errors of AI can be fatal. With the incorporation of AI in
almost every sector of our everyday life, we need to keep asking
ourselves— can we trust AI, and how much?
This Editorial outlines the importance of openness and transparency
in the development and applications of AI to allow all users to fully
understand both the benefits and risks of this ubiquitous technology,
and outlines how the Artificial Intelligence and Machine Learning
Gateway on F1000Research meets these needs.
Keywords
artificial intelligence, machine learning, open data, open research,
Artificial Intelligence and Machine Learning Gateway, sharing
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F1000Research 2023, 12:387 Last updated: 27 NOV 2023
This article is included in the Research on
Research, Policy & Culture gateway.
This article is included in the Artificial
Intelligence and Machine Learning gateway.
This article is included in the Artificial
Intelligence in Academic Research collection.
Corresponding authors: Sally R. Ellingson (sel228@uky.edu), Parag Chatterjee (paragc@ieee.org)
Author roles: Rodgers CM: Writing – Original Draft Preparation, Writing – Review & Editing; Ellingson SR: Writing – Review & Editing;
Chatterjee P: Writing – Review & Editing
Competing interests: Caellin M Rodgers helped in preparing the first draft of this article. Caellin M Rodgers is a Publishing Executive at
F1000. She did not handle the editorial processing of this article in any way.
Grant information: The author(s) declared that no grants were involved in supporting this work.
Copyright: © 2023 Rodgers CM et al. This is an open access article distributed under the terms of the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite this article: Rodgers CM, Ellingson SR and Chatterjee P. Open Data and transparency in artificial intelligence and
machine learning: A new era of research [version 1; peer review: not peer reviewed] F1000Research 2023, 12:387
https://doi.org/10.12688/f1000research.133019.1
First published: 12 Apr 2023, 12:387 https://doi.org/10.12688/f1000research.133019.1
Page 2 of 5F1000Research 2023, 12:387 Last updated: 27 NOV 2023
Open Science is becoming the new funding ‘norm’
Many large-scale funding bodies have, or are moving towards, new Open Science mandates in their funding require-
ments, including the European Commission (European Commission, 2022) and the US government (National Institutes
of Health, 2023). To meet these requirements, all data utilised in research outputs should conform to the FAIR principles,
(Wilkinson et al., 2016), meaning that it should be Findable, Accessible, Interoperable and Reusable. In addition to Open
data, to meet Open Science requirements, manuscripts should be published Open Access and underlying software or code
utilised in producing the research output should be produced using an open-source option where possible.
For many researchers working in the field of Artificial Intelligence (AI), these concepts are not foreign – for example, the
open-source operating system Linux (Wilson et al., 2016) is a popular choice amongst computer scientists, and many
share their codes freely on sites such as GitHub, BitBucket or similar. For others, especially those with more industry-
focused roles who are often working to achieve patents, Open Science, and in particular data sharing, may be a newer
concept. Luckily, there are plenty of resources freely available to help prospective authors with the new requirements of
their funding bodies, including F1000’s data policies (Grant, 2022).
The European Commission (European Commission Publications Office, 2018) states that Open Data and data sharing are
particularly important for AI and machine learning research due to the large volumes of data required to train machines up
to a usable standard. This is particularly notable with respect to the recent rise of AI art, which is trained on art without the
permission of the original artists (Ghosh & Fossas, 2022), and may occur in other disciplines as well without either freely
shared data to train the AI on and/or modern copyright legislation to protect others’ intellectual property rights.
F1000’s publication model as a means of addressing Open Science requirements
Unlike many traditional publication models, the F1000Research model is designed specifically for the free sharing of
research and its associated materials. It does this in four main ways.
Firstly, through use of Open Access – all articles published on F1000Research are freely available for all at point of
publication. By removing the paywalls of traditional publishing, it means that researchers in under-funded countries
or institutions also have access to valuable resources and reference work. Additionally, and of particular importance
to AI research, news reporters and the general public also have access to this work, giving them the ability to read and
understand discussions around AI directly from the source.
Secondly, F1000Research has strict Open Data mandates in line with the current updates to various funding mandates
listed above. These mandates require that all data produced or used as part of a research work is available to peer
reviewers, readers and researchers interested in reproducing or verifying the research. F1000Research operates under an
‘as open as possible, as closed as necessary’ policy, meaning that all material, including material that can’t be shared (for
example, because it contains sensitive patient data) must be declared in the data availability statement along with any
reason why it is unable to be shared. More available data is generally excellent news for researchers working in machine
learning!
Thirdly, the publication model for F1000Research allows for a huge range of article types across the full research
spectrum to be published. This means that valuable studies don’t have to wait until they’re ready to be turned into an
original research article to start sharing data – methods articles, data notes and similar are all valuable ways that
researchers can share their work prior to the final research article. The model also allows for versioning, which is great for
researchers working in AI as it allows for updates to be made to articles as the field evolves, rather than requiring a full
new article with only a minor improvement.
Finally, F1000Research employs Open Peer Review. This has several benefits to authors, readers and peer reviewers.
For authors, it means that their work can be published online faster as peer review – typically the slowest part in the
publication process – occurs after publication. For readers, it means that the full discussion about the article’s merits (and
potential oversites or flaws) is freely available to read, allowing them the autonomy to decide for themselves about the
article’s quality. For the reviewers themselves, it means they receive full credit for the work they put into this extremely
important part of the research publication process, as their review is also published online and can be read and cited.
All these aspects are crucial for fostering an open, sharing research culture, essential to many new funder mandates and
incredibly useful for AI researchers who often require a wealth of freely available data for quality machine learning.
The future of the Artificial Intelligence and Machine Learning Gateway
Artificial intelligence and machine learning will continue to be at the forefront of the development of new computer-based
technologies for the foreseeable future. However, with the issues arising from unconsidered applications, like students
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using ChatGPT to write homework essays (Cotton et al., 2023) and the potential for a rise in AI-generated research
articles with very little oversight (Dergaa et al., 2023), it is important to share as much research and data openly as
possible. This allows users of the technology to fully understand the work and data behind the technology, allows legal
experts and policymakers across both governments and industry to ensure their guidance is current and relevant, and it
allows other AI researchers to make further developments and decisions based on a thorough understanding of what has
come before. Ethical considerations and discussion, as well as topics on any of the above are welcome in the Artificial
Intelligence and Machine Learning Gateway.
The F1000Research model is particularly well-suited to facilitate the open, rapid dissemination of research works
related to AI and related fields, and its applications.
Firstly, following the high-level policy decisions made by many funders already, it is essential to have a publication venue
that already complies with their new requirements, and has done for many years (Pencheva et al., 2018). This ensures that
publications meeting these requirements are handled smoothly and expertly by F1000Research’s internal editors, who
will guide authors through ensuring they comply with their funding mandates.
Secondly, trustworthy data for AI is of utmost importance to ensure accurate and efficient machine learning strategies
(Liang et al., 2022). While outliers or ‘untrustworthy’ data is less important in human education, due to our ability to
recognize oddities or issues with datasets, AI takes all data at face value unless taught otherwise. Therefore, clean data is
especially important when creating new AI software for new applications. Fostering the sharing of data that has already
been analyzed and understood, and potentially used to train AI, is a cornerstone of the F1000Research model and why this
Gateway has the potential to be a go-to resource for AI scholars and industry professionals alike.
Finally, most current publication venues are limited in publications to technical or applied research and excluding
relevant contributions from the humanities and social sciences. The Artificial Intelligence and Machine Learning
Gateway has the scope and interdisciplinary nature required to foster important sociological and other humanities
research as well, providing a space where important discussions can occur about the ethics, legality and social aspects of
new AI-powered technologies.
Therefore, the Artificial Intelligence and Machine Learning Gateway will provide an Open Science space for interdis-
ciplinary publications on all aspects of AI, bridging the many different communities to create open and thoughtful
discussions. It can provide a useful resource and collaborative space to guide researchers, authors and industry
professionals through this new era of artificial intelligence.
Data availability
No data are associated with this article.
References
Cotton D, Cotton P, Shipway J: Chatting and cheating: Ensuring Liang W, Tadesse G, Ho D, et al.: Advances, challenges and opportunities
academic integrity in the era of ChatGPT. Innov. Educ. Teach. Int. 2023; in creating data for trustworthy AI. Nat. Mach. Intell. 2022; 4: 669–677.
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Publisher Full Text National Institutes of Health: Data Management & Sharing Policy
Dergaa I, Chamari K, Zmijewski P, et al.: From human writing to artificial Overview. 2023.
intelligence generated text: examining the prospects and potential Reference Source
threats of ChatGPT in academic writing. Biol. Sport. 2023; 40(2): 615–622. Pencheva I, Esteve M, Mikhaylov SJ: Big Data and AI – A transformational
Publisher Full Text shift for government: So, what next for research? Public Policy Adm.
European Commission Publications Office: AI and Open Data: a crucial 2018; 35(1): 24–44.
combination. data.europa.eu - The official portal for European data. 2018. Publisher Full Text
Reference Source Wilkinson M, Dumontier M, Aalbersberg I, et al. : The FAIR Guiding
European Commission: Data Act: Commission proposes measures for a Principles for scientific data management and stewardship. Sci. Data.
fair and innovative data economy. European Commission Press Release. 2016; 3: 160018.
2022. PubMed Abstract|Publisher Full Text|Free Full Text
Reference Source Wilson G, Bryan J, Cranston K, et al.: Good enough practices in scientific
Ghosh A, Fossas G: Can There be Art Without an Artist? arXiv. 2022; arXiv: computing. PLoS Comput. Biol. 2016; 13: e1005510.
2209.07667v2. PubMed Abstract|Publisher Full Text|Free Full Text
Grant R: Data management supporting the research communications
ecosystem. 2022.
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