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rluislopez.bsky.social
@rluislopez.bsky.social
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olivia.science
Finally! 🤩 Our position piece: Against the Uncritical Adoption of 'AI' Technologies in Academia:
doi.org/10.5281/zeno...

We unpick the tech industry’s marketing, hype, & harm; and we argue for safeguarding higher education, critical
thinking, expertise, academic freedom, & scientific integrity.
1/n
Abstract: Under the banner of progress, products have been uncritically adopted or even imposed on users — in past centuries with tobacco and combustion engines, and in the 21st with social media. For these collective blunders, we now regret our involvement or apathy as scientists, and society struggles to put the genie back in the bottle. Currently, we are similarly entangled with artificial intelligence (AI) technology. For example, software updates are rolled out seamlessly and non-consensually, Microsoft Office is bundled with chatbots, and we, our students, and our employers have had no say, as it is not considered a valid position to reject AI technologies in our teaching and research. This is why in June 2025, we co-authored an Open Letter calling on our employers to reverse and rethink their stance on uncritically adopting AI technologies. In this position piece, we expound on why universities must take their role seriously toa) counter the technology industry’s marketing, hype, and harm; and to b) safeguard higher education, critical thinking, expertise, academic freedom, and scientific integrity. We include pointers to relevant work to further inform our colleagues. Figure 1. A cartoon set theoretic view on various terms (see Table 1) used when discussing the superset AI (black outline, hatched background): LLMs are in orange; ANNs are in magenta; generative models are in blue; and finally, chatbots are in green. Where these intersect, the colours reflect that, e.g. generative adversarial network (GAN) and Boltzmann machine (BM) models are in the purple subset because they are both generative and ANNs. In the case of proprietary closed source models, e.g. OpenAI’s ChatGPT and Apple’s Siri, we cannot verify their implementation and so academics can only make educated guesses (cf. Dingemanse 2025). Undefined terms used above: BERT (Devlin et al. 2019); AlexNet (Krizhevsky et al. 2017); A.L.I.C.E. (Wallace 2009); ELIZA (Weizenbaum 1966); Jabberwacky (Twist 2003); linear discriminant analysis (LDA); quadratic discriminant analysis (QDA). Table 1. Below some of the typical terminological disarray is untangled. Importantly, none of these terms are orthogonal nor do they exclusively pick out the types of products we may wish to critique or proscribe. Protecting the Ecosystem of Human Knowledge: Five Principles
September 6, 2025 at 8:13 AM
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kul-ai-sschool.bsky.social
What can we learn from algorithmic scandals in digital welfare states?

In this blogpost, @maartenbouwmeester.bsky.social discusses the constitutional reforms following the #AI-related childcare benefit scandal in the Netherlands.

Read it here: www.law.kuleuven.be/ai-summer-sc....
June 24, 2025 at 7:01 AM
Reposted
kul-ai-sschool.bsky.social
A warm welcome for all our participants to the 5th edition of the KU Leuven #SummerSchool on the Law, Ethics and Policy of #AI!

10 days. 35 nationalities. 65 participants. 45 lecturers. And lots of fascinating topics… Here we go🎒😊

More info about the AI Summer School: lnkd.in/euH3NWh
June 30, 2025 at 12:36 PM
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kul-ai-sschool.bsky.social
Wonderful to see so many happy faces during our
@kul-ai-sschool.bsky.social online Alumni Networking event - for the 4th year in a row🎉

Thanks to all our fantastic participants! We love the AI Summer School Community🥰

See you again soon!
July 3, 2025 at 7:07 AM