How to talk about "AI" without adding to the anthropomorphization • Buttondown

Mystery AI Hype Theater 3000: The Newsletter

June 25, 2026

How to talk about "AI" without adding to the anthropomorphization

Emily M. Bender and Nanna Inie

In our op-ed for Tech Policy Press ("We Need to Talk About How We Talk About 'AI'"), we made the case against the anthropomorphizing language that makes it harder to have clear discussions of what so-called "AI" technologies actually do, and when and whether to use them. But these ways of speaking are deeply ingrained at this point, and it takes work carve new conversational and writing habits. That work involves at least three steps:

Noticing which word choices are anthropomorphizing

Finding alternatives

Getting in the habit of using the alternatives

In our research (summarized in the op-ed) we have been working on the first two steps, categorizing the kinds of anthropomorphizing language and using those categories to organize potential alternatives.

De-anthropomorphizing language talks about computer systems in terms of their functionality (what people build and/or use them to do), assigns agency to people using systems and not systems, and avoids aggrandizing metaphors about cognition.

We aim to find substitutes that are as self-explanatory as possible, so that you can just go ahead and use them without having to explain. (Though of course, if someone asks "Why are you calling it that?" that's also a great opening.)

Some of these rephrasings may feel a little clunky, and they can end up longer than the anthropomorphizing shorthand. This means it takes a little more dedication to use them, but also isn't necessarily a bad thing. We should stop and think about the tech we are using, or even discussing, and what it actually does.

We'll go through the categories of anthropomorphizing language we identified in Inie et al 2026, and give examples of de-anthropomorphized versions for each.

Our suggestions

Cognizer and products of cognition

This category is super frequent, because it's right in the marketing term artificial intelligence itself. This is language that locates thinking in an algorithm. Instead, we recommend describing software as performing calculations or other algorithmic operations, and locate the thinking with the people using the system. (In some cases, people clearly aren't thinking when they use them, but they are still the ones who should be.)

Examples:

artificial intelligence → probabilistic automation<br>hybrid intelligence → augmented human intelligence<br>image recognition → image labeling<br>speech recognition → automatic transcription<br>the model shows bias → the model reflects bias<br>model mistakes → model errors<br>chatbots are good at … → chatbots are good for …<br>hallucination → undesirable output

In general, we recommend avoiding using artificial intelligence or AI in reference to technologies. We do still talk about the AI industry, because that is the name of a thing, and talk about AI as an ideology. But when the intended referent is some specific technological system, it is always better to name that system itself. Maybe it's some specific product. Or maybe it's a system with a particular function like automatic transcription. Either way, it's worth finding names that aren't also anthropomorphizing. If you need a more general term, our recommendation of probabilistic automation above works for many (but not all) things sold as "AI".

We've also put hallucination in this category, because in its original sense it refers to perceiving things that are not there, but of course software systems (and conversation simulators in particular) don't perceive anything. Our proposed one-to-one replacement phrase is undesirable outputs, but it is also important to know that all LLM output is probablisitically produced synthetic text; there is no fundamental difference between desirable and undesirable outputs on the system side, but only for the people interpreting them.

Emotion

These are turns of phrase that suggest that software systems have emotional lives. We don't have particular rephrasings to recommend here because there is no accurate way to talk about emotional states of computers other than to reassert the obvious, that they don't have any. What's perhaps most subtle (and thus most fun for linguists) about this category is that these allusions to emotional experience can sneak in in surprising ways: If you say that ChatGPT struggles to do something, or that you had to coax it into some output, you are describing it as if it had emotional states.

Communication

In this category, we find words that place automated systems, usually synthetic text extruding machines, on an equal footing with people in communicative situations. If we ask something of Claude, we are describing Claude as a conversational partner. Instead of verbs like ask, say, inform, discuss, use verbs appropriate to computers like input and output. Another strategy is to foreground...