Archives des mots-clés : Artificial intelligence

Open Science at the Generative AI Turn: An Exploratory Analysis of Challenges and Opportunities

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Authors : Mohammad Hosseini, Serge P.J.M. Horbach, Kristi L. Holmes, Tony Ross-Hellauer

Technology influences Open Science (OS) practices, because conducting science in transparent, accessible, and participatory ways requires tools/platforms for collaborative research and sharing results. Due to this direct relationship, characteristics of employed technologies directly impact OS objectives. Generative Artificial Intelligence (GenAI) models are increasingly used by researchers for tasks such as text refining, code generation/editing, reviewing literature, data curation/analysis.

GenAI promises substantial efficiency gains but is currently fraught with limitations that could negatively impact core OS values such as fairness, transparency and integrity, and harm various social actors. In this paper, we explore possible positive and negative impacts of GenAI on OS.

We use the taxonomy within the UNESCO Recommendation on Open Science to systematically explore the intersection of GenAI and OS. We conclude that using GenAI could advance key OS objectives by further broadening meaningful access to knowledge, enabling efficient use of infrastructure, improving engagement of societal actors, and enhancing dialogue among knowledge systems.

However, due to GenAI limitations, it could also compromise the integrity, equity, reproducibility, and reliability of research, while also having potential implications for the political economy of research and its infrastructure. Hence, sufficient checks, validation and critical assessments are essential when incorporating GenAI into research workflows.

URL : Open Science at the Generative AI Turn: An Exploratory Analysis of Challenges and Opportunities

DOI : https://doi.org/10.31235/osf.io/zns7g

Data Science and AI in Context: Summary and Insights

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Author : Alfred Spector

This paper explores how to deploy data science and data-driven AI, focusing on the broad collection of considerations beyond those of statistics and machine learning. Building on an analysis rubric introduced in a recent textbook by the author and three others, this paper summarizes some of the book’s key points and adds reflections on AI’s extraordinary growth and societal effects. The paper also discusses how to balance inevitable trade-offs and provides further thoughts on societal implications.

DOI : https://doi.org/10.1162/99608f92.cdebd845

Academic Integrity and Artificial Intelligence in Higher Education (HE) Contexts: A Rapid Scoping Review

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Authors : Beatriz Antonieta Moya, Sarah Elaine Eaton, Helen Pethrick, K. Alix Hayden, Robert Brennan, Jason Wiens, Brenda McDermott

Artificial Intelligence (AI) developments challenge higher education institutions’ teaching, learning, assessment, and research practices. To contribute timely and evidence-based recommendations for upholding academic integrity, we conducted a rapid scoping review focusing on what is known about academic integrity and AI in higher education. We followed the Updated Reviewer Manual for Scoping Reviews from the Joanna Briggs Institute (JBI) and the Preferred Reporting Items for Systematic reviews Meta-Analysis for Scoping Reviews (PRISMA-ScR) reporting standards.

Five databases were searched, and the eligibility criteria included higher education stakeholders of any age and gender engaged with AI in the context of academic integrity from 2007 through November 2022 and available in English. The search retrieved 2223 records, of which 14 publications with mixed methods, qualitative, quantitative, randomized controlled trials, and text and opinion studies met the inclusion criteria. The results showed bounded and unbounded ethical implications of AI.

Perspectives included: AI for cheating; AI as legitimate support; an equity, diversity, and inclusion lens into AI; and emerging recommendations to tackle AI implications in higher education. The evidence from the sources provides guidance that can inform educational stakeholders in decision-making processes for AI integration, in the analysis of misconduct cases involving AI, and in the exploration of AI as legitimate assistance. Likewise, this rapid scoping review signals key questions for future research, which we explore in our discussion.

URL : Academic Integrity and Artificial Intelligence in Higher Education (HE) Contexts: A Rapid Scoping Review

DOI : https://doi.org/10.55016/ojs/cpai.v7i3.78123

PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge

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Authors  : Chih-Hsuan Wei, Alexis Allot, Po-Ting Lai, Robert Leaman, Shubo Tian, Ling Luo, Qiao Jin, Zhizheng Wang, Qingyu Chen, Zhiyong Lu

PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases and chemicals. It currently provides over one billion entity and relation annotations across approximately 36 million PubMed abstracts and 6 million full-text articles from the PMC open access subset, updated weekly.

PubTator 3.0’s online interface and API utilize these precomputed entity relations and synonyms to provide advanced search capabilities and enable large-scale analyses, streamlining many complex information needs. We showcase the retrieval quality of PubTator 3.0 using a series of entity pair queries, demonstrating that PubTator 3.0 retrieves a greater number of articles than either PubMed or Google Scholar, with higher precision in the top 20 results.

We further show that integrating ChatGPT (GPT-4) with PubTator APIs dramatically improves the factuality and verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive set of features and tools that allow researchers to navigate the ever-expanding wealth of biomedical literature, expediting research and unlocking valuable insights for scientific discovery.

URL : PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge

DOI : https://doi.org/10.1093/nar/gkae235

Using AI to solve business problems in scholarly publishing

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Author: Michael Upshall

Artificial intelligence (AI) tools are widely used today in many areas, and are now being introduced into scholarly publishing. This article provides a brief overview of present-day AI and machine learning as used for text-based resources such as journal articles and book chapters, and provides an example of its application to identify suitable peer reviewers for manuscript submissions.

It describes how one company, UNSILO, has created a tool for this purpose, and the underlying technology used to deliver it. The article also offers a glimpse into a future where AI will profoundly change the way that academic publishing will work.

URL : Using AI to solve business problems in scholarly publishing

DOI : http://doi.org/10.1629/uksg.460

Artificial intelligence in peer review: How can evolutionary computation support journal editors?

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Authors : Maciej J. Mrowinski, Piotr Fronczak, Agata Fronczak, Marcel Ausloos, Olgica Nedic

With the volume of manuscripts submitted for publication growing every year, the deficiencies of peer review (e.g. long review times) are becoming more apparent. Editorial strategies, sets of guidelines designed to speed up the process and reduce editors workloads, are treated as trade secrets by publishing houses and are not shared publicly.

To improve the effectiveness of their strategies, editors in small publishing groups are faced with undertaking an iterative trial-and-error approach. We show that Cartesian Genetic Programming, a nature-inspired evolutionary algorithm, can dramatically improve editorial strategies.

The artificially evolved strategy reduced the duration of the peer review process by 30%, without increasing the pool of reviewers (in comparison to a typical human-developed strategy).

Evolutionary computation has typically been used in technological processes or biological ecosystems. Our results demonstrate that genetic programs can improve real-world social systems that are usually much harder to understand and control than physical systems.

URL : https://arxiv.org/abs/1712.01682