The psychic structure of disciplinary imperialism

From Sherry Turkle’s classic The Second Self pg 229-230:

The first justification for AI’s invasions and colonization of other disciplines’ intellectual turf was a logic of necessity. The excursions into psychology and linguistics began as raids to acquire ideas that might be useful for building thinking machines. But the politics of “colonization” soon takes on a life of its own. The invaders come not only to carry off natural resources but to replace native “superstitions” with their “superior” world view. AI first declared the need for psychological theories that would work on machines. The next step was to see these alternatives as better—better because they can be “implemented,” better because they are more “scientific.” Being in a colonizing discipline first demands and then encourages an attitude that might be called intellectual hubris. You need intellectual principles that are universal enough to give you the feeling that you have something to say about everything. The AI community had this in their idea of program. Furthermore, since you cannot master all the disciplines that you have designs on, you need confidence that your knowledge makes the “traditional wisdom” of these fields unworthy of serious consideration. Here too, the AI scientist feels that seeing things through a computational prism so fundamentally changes the rules of every game in the social and behavioral sciences that everything that came before is relegated to a period of intellectual immaturity. And finally you have to feel that nothing is beyond your intellectual reach if you are smart enough.

See also the hostility of digital elites towards expertise.

#dataScience #digitalElites #disciplines #domainExpertise #epistemicHeirarchy #intellectualLabour #intellectualLife #work

Three modes of working with LLMs in higher education

I’m enjoying this series by Anthropic, even if it’s largely a new language for things I’ve already argued in Generative AI for Academics. I like their description of three modes of working with LLMs:

• Automation: outsourcing a task to the LLM
• Augmentation: working as a collaborator as a thinking partner
• Agency: acting quasi-autonomously to pursue a goal

In these terms my stance has been that augmentation offers tremendous intellectual possibilities for academic work but that the political economy of academic labour tends people towards automation and (eventually) agency. At best these can be short-term helpful for individuals but the proportion of automation and (AI) agency in organisations likely correlates with deprofessionalisation, dehumanisation of working life and all sorts of incredibly specific pathologies generated as a byproduct of using LLMs.

https://www.youtube.com/watch?v=4szRHy_CT7s&list=PLf2m23nhTg1NjL3-jL3s0qZCYzO07ZQPv&index=3

I thought this was helpful for thinking about different steps in using LLMs:

• Delegation: identify what the tasks are and how they should enacted, either individually, in collaboration with an LLM or outsourced to an LLM
• Description: describing the task precisely to the LLM in a way conducive to getting it to meet your expectations
• Discernment: evaluating what’s useful from what’s not through reflection on LLM outputs, using domain knowledge
• Diligence: cultivating a reflective and ethical approach to the whole workflow using LLMs

The problem with systems like Copilot is that they are geared together simplifying/constraining augmentation while pushing people towards automation and agency. They take responsibility for delegation from the individual and instead scaffold it through the affordances embedded in familiar software. It’s a recipe for outsourcing labour and we shouldn’t be encouraging it.

The political economy of these modes are different: description and discernment, as well as augmentation more broadly, presuppose domain expertise and existing practical knowledge. Whereas delegation and automation/agency tend to rendering that domain knowledge redundant, pushing it aside and generally obliterating it as an organisational value.

https://www.youtube.com/watch?v=W4Ua6XFfX9w&list=PLf2m23nhTg1NjL3-jL3s0qZCYzO07ZQPv&index=4

This is exactly what I’ve meant when I talk about reflexivity in relation to prompting. Perhaps I should drop the (essentially theoretical) language of ‘reflexivity’ and instead talk about ‘problem awareness’ in future training.

#agency #AIFluency #augmentation #automation #domainExpertise #knowledge