How could we show that AI could be otherwise? That was the question I kept returning to as discussant for the panel The matter of methods in researching AI, organized by Tobias Blanke, Annalisa Pelizza and Claudia Aradau at EASST 2026 in Kraków. These are my concluding comments. My answer, in short: a politics of choice.
What does a method reveal and hide?
STS was built on a reaction against an overly stable account of science and technology. Our project was to show that choices were made, and that things could have been otherwise.
So how does STS react when the field tells us that AI is a black box? How do we disentangle the thingness of AI? When we look closely, we see how people struggle to put these technologies together. And we see how people struggle to integrate them into their work practices.
Where do we enter?
What is the entry point for studying AI systems? Lotta Björklund Larsen and I asked a version of this question in How should we theorize algorithms? Each entry point shows something different:
- Going under the hood—algorithmic ethics, auditing—shows us injustices, but not how they came about.
- Looking at the interactions around AI shows the invisible work of making AI work with us: perhaps all the human labor it takes to make these “autonomous” machines work.
- Following the actors—the tinkering and work of constructing AI—lets us show that people work to make these machines.
Tinkering, and the moments of choice
What kinds of choices are made when AI is built? In The practices and politics of machine learning I tried to write a field guide to the moments of choice and conflict in building AI systems.
It is not an innocent choice to vectorize. Or to choose features. Or to decide on temporalities. Or to assume a Gaussian curve. When we follow the construction of AI, we can show just how many choices are made along the way. The papers in the panel gave lovely examples of exactly this.
The politics of absence
But where is the politics of choice? One answer is to ask what is made absent. In Ontological overflows and the politics of absence I traced how absences are made—and layered—in the systems we use to construct pandemics. The same thing happens when synthetic datasets are constructed: the tails of the distribution are cut off. Saghi Hajisharif, Ericka Johnson and I show how in The ontological politics of synthetic data, where the outliers disappear and intersectional hallucinations appear.
The epistemic trap of the vernacular
Our own concepts can be traps too. With David Ribes, in Computational universalism, and in conversation with Lucy Suchman’s piece on the uncontroversial “thingness” of AI, I have been thinking about concepts as epistemic traps. In Enacting the pandemic the trap was “the algorithm.” Today it might be “ground truthing” or “datafication.”
When we take up the vernacular of the field, do we risk stabilizing the very things we want to show could be otherwise? Talking about ground truths stabilizes the notion that there are ground truths. Naming invisible work, or a creole or pidgin, is also a way of seeing—emic and etic categories do different work.
What do we win and lose?
So what do we win and lose when we choose a particular method, or a particular theory? How can we stay with the original political project of showing that things could be otherwise?
Is it worth staying with? I think so! The point is that we have a choice—to make other choices—when AI is made and stabilized. Then we have to reflect on what our methods allow us to do. So that we can show that it could be otherwise. And make a better AI society.