{"id":25745,"date":"2025-04-14T14:44:51","date_gmt":"2025-04-14T06:44:51","guid":{"rendered":"https:\/\/medicine.nus.edu.sg\/cbme\/?page_id=25745"},"modified":"2025-04-24T15:46:58","modified_gmt":"2025-04-24T07:46:58","slug":"adapting-to-ai-assisted-and-automated-academia","status":"publish","type":"page","link":"https:\/\/medicine.nus.edu.sg\/cbme\/adapting-to-ai-assisted-and-automated-academia\/","title":{"rendered":"Adapting to AI-Assisted (and Automated?) Academia"},"content":{"rendered":"\t\t
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Date:<\/strong> Tuesday, 6 May 2025<\/span>
Time:<\/strong> 10.30am – 11.30am<\/span>
Venue:<\/strong> COM3 Meeting Room 20, (#02-59) 11 Research Link, Singapore 119391<\/span><\/h5>\n

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Abstract<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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William Gibson is reported to have said that \u201cthe future is already here \u2013 it’s just not very evenly distributed.\u201d With survey results showing widespread use of LLMs in both scholarly writings and peer review, the point appears apt to academic contexts.
Just two years ago, a talk on the ethics of LLM use in academia would have touched upon important yet well-known issues: biases, hallucinations, the impact on publish-or-perish dynamics, and similar concerns. We might have even debated whether these models should be used at all. Today, however, a different set of questions appears more relevant. With sakana.ai claiming to have published the first fully AI-generated paper that passed peer review, and evidence indicating that LLM performance on complex cognitive tasks doubles approximately every seven months, we must now seriously consider what happens as LLMs begin fully automating first specific parts of academic processes, and eventually academia as a whole. Traditional notions of merit, evaluation, production, peer review, and grant applications may soon lose their meaning or ecological validity. Those who master these changes stand to benefit immensely, while those who do not risk being left behind. We face the prospect of a digital divide so significant that it evokes Nick Agar’s concerns regarding enhancement potentially leading to two separate human species. How should we navigate this transition?
Drawing on our work with AUTOGEN, ensemble workflow methods, proposed guidelines for the ethical use of LLMs, the complexities of assigning credit and blame in AI-assisted work, and related efforts, this talk outlines the current technological capacities and the state of the ongoing ethical debate. However, its primary focus is on exploring deeper questions about the future of scientific progress and academic labor.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Dr Sebastian Porsdam Mann\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Postdoctoral researcher at CeBIL,
University of Copenhagen
Visitng Research Fellow,
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Biography<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Sebastian Porsdam Mann is a postdoctoral researcher at CeBIL, University of Copenhagen. With an academic background in philosophy, neuroscience, and applied ethics (BA, PhD, University of Cambridge) and recently defended doctoral research in international human rights law (DPhil pending, University of Oxford), he brings a rich interdisciplinary perspective to his work. His postdoctoral appointments include positions at Harvard Medical School, the University of Oxford, and the University of Copenhagen (supported by a personal grant from the Carlsberg Foundation).<\/p>\n

His research focuses on exploring the practical potential and regulatory conditions necessary for novel technologies\u2014such as blockchain and generative artificial intelligence\u2014to advance scientific progress and uphold normative goals, particularly in relation to the human right to science. Porsdam Mann is co-author\/editor of The Right to Science: Then and Now (Cambridge University Press, 2021) and Scientific Freedom: The Heart of the Right to Science (Rowman & Littlefield, 2023). His scholarly contributions have been featured in prominent journals, including Nature Machine Intelligence, npj Digital Medicine, NEJM AI, PNAS, Philosophical Transactions of the Royal Society, Human Rights Quarterly, Molecular & Cellular Proteomics, and the American Journal of Bioethics. His work has a field-weighted citation impact score of 5.22 (SciVal, 2021\u20132025).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Professor Simon Chesterman<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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David Marshall Professor and Vice Provost (Educational Innovation)
National University of Singapore\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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Biography<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Simon Chesterman is David Marshall Professor and Vice Provost (Educational Innovation) at the National University of Singapore, where he is also the founding Dean of NUS College. He serves as Senior Director of AI Governance at AI Singapore and Editor of the Asian Journal of International Law. Previously, he was Dean of NUS Law from 2012 to 2022 and Co\u2010President of the Law Schools Global League from 2021 to 2023.<\/p>\n

Educated in Melbourne, Beijing, Amsterdam, and Oxford, Professor Chesterman\u2019s teaching experience includes periods at the Universities of Melbourne, Oxford, Southampton, Columbia, and Sciences Po. From 2006\u20102011, he was Global Professor and Director of the New York University School of Law Singapore Programme. Prior to joining NYU, he was a Senior Associate at the International Peace Academy and Director of UN Relations at the International Crisis Group in New York.<\/p>\n

Professor Chesterman is the author or editor of more than twenty books, including We, the Robots? Regulating Artificial Intelligence and the Limits of the Law (CUP, 2021) and One Nation Under Surveillance (OUP, 2011). He also writes on legal education and higher education more generally, and is the author of five novels including the Raising Arcadia trilogy and Artifice.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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