Category: Higher Ed

Dan McQuillan writes, The role of the University is to resist AI,following themes from Ivan Illich’s ‘Tools for Conviviality’.

It’s a scathing overview with points that I think many others wonder about (although in less concrete ways than McQuillan).

Contemporary AI is a specific mode of connectionist computation based on neural networks and transformer models. AI is also a tool in Illich’s sense; at the same time, an arrangement of institutions, investments and claims. One benefit of listening to industry podcasts, as I do, is the openness of the engineers when they admit that no-one really knows what’s going on inside these models.

Let that sink in for a moment: we’re in the midst of a giant social experiment that pivots around a technology whose inner workings are unpredictable and opaque.

The highlight is mine. I agree that there’s something disconcerting about using systems that we don’t understand fully.

Generative AI’s main impact on higher education has been to cause panic about students cheating, a panic that diverts attention from the already immiserated experience of marketised studenthood. It’s also caused increasing alarm about staff cheating, via AI marking and feedback, which again diverts attention from their experience of relentless and ongoing precaritisation.

The hegemonic narrative calls for universities to embrace these tools as a way to revitalise pedagogy, and because students will need AI skills in the world of work. A major flaw with this story is that the tools don’t actually work, or at least not as claimed.

AI summarisation doesn’t summarise; it simulates a summary based on the learned parameters of its model. AI research tools don’t research; they shove a lot of searched-up docs into the chatbot context in the hope that will trigger relevancy. For their part, so-called reasoning models ramp up inference costs while confabulating a chain of thought to cover up their glaring limitations.

I think there are philosophical questions here worth considering. Specifically, the postulation that AI simply “simulates” is too simple and not helpful. What is a photograph? It’s a real thing, but not the real thing captured on the image. What is a video played on a computer screen? It’s a real thing, but it’s not the real thing. The photo and screen simulate the real world, but I’m not aware of modern philosophers critiquing these forms of media. (I’d suspect that earlier media theorists did just that until the media was accepted en masse by society.)

He goes on to cite environmental concerns (although as I posted recently, the questions of water consumption are exaggerated) among things we’re well suited to take heed of. His language is perhaps a bit too revolutionary.

As for people’s councils — I am less sanguine that these have much utility.

Instead of waiting for a liberal rules-based order to magically appear, we need to find other ways to organise to put convivial constraints into practice. I suggest that a workers’ or people’s council on AI can be constituted in any context to carry out the kinds of technosocial inquiry advocated for by Illich, that the act of doing so prefigures the very forms of independent thought which are undermined by AI’s apparatus, and manifests the kind of careful, contextual and relational approach that is erased by AI’s normative scaling.

I suspect that people’s councils are glorified committees — structures that are kabuki theater than anything else and will struggle to align with the speed at which AI tools are emerging.

The role of the university isn’t to roll over in the face of tall tales about technological inevitability, but to model the forms of critical pedagogy that underpin the social defence against authoritarianism and which makes space to reimagine the other worlds that are still possible.

I don’t share all of his fears, but it’s important to consider voices that may not align with a techno-optimistic future.