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:
1. Noticing which word choices are anthropomorphizing 2. Finding alternatives 3. 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_**
**_hybrid intelligence_** → **_augmented human intelligence_**
**_image recognition_** → **_image labeling_**
**_speech recognition_** → **_automatic transcription_**
**_the model shows bias_** → **_the model reflects bias_**
**_model mistakes_** → **_model errors_**
**_chatbots are good at …_** → **_chatbots are good for …_**
**_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 the fact of simulation.
Examples:
**_prompt_** → **_text input_**
**_answer_** → **_output_**
**_chatbot / conversational agent_** → **_conversation simulator_**
Agency
Turns of phrase that locate agency with a machine often serve to obfuscate the interests and goals of people. We suggest revising to locate agency with people or choosing less agentive verbs.
Examples:
**_ChatGPT assisted students_** → **_the students used ChatGPT_**
**_revealing the solution_** → **_displaying the solution_**
**_AI agent_** → **_probabilistic, unverified software manipulator_**
The elephant in the room of this category is the buzzword _AI agent_ (and its variants like _agentic AI systems_). This is a term for software systems that connect LLMs (probabilistic synthetic text extruding machines) and/or other components up with other systems that can impact the world, i.e. systems previously designed for people to do things like schedule appointments, book flights, or make other purchases. Our suggestion for this one for now is _probabilistic, unverified software manipulator_, which has the advantage of giving a suitably gross acronym ("No thank you, I don't want to use your PUSMic system.") But, we are definitely open to other ideas! Send them our way and if any seem particularly apt, we will add them to this list.
Human Role Analogy
These are words that cast systems as doing the same work as people in various roles, and serve to hide all of the ways in which such automation falls short of what is needed all the while devaluing the actual work that people do and relationships that we form. Calling systems _tutor_ or _co-creator_ are overclaims that describe what a developer might wish they could develop—for those who want to replace people in these roles.
For this category, our recommendation is to use language that describes algorithms as tools (or products) that people use, rather than as human-like entities, and more clearly indicates system functionality while also not telegraphing a plan to replace people.
Names and Pronouns
The names and pronouns we use to refer to systems can also function in anthropomoprhizing ways. With system names, its somewhat trickier, because the system developers usually get to do the naming, and if they use a person's name for it, everyone else is stuck repeating that anthropomorphizing choice (we're looking at you, Anthropic, with _Claude_) or going for circumlocutions (_Anthropic's conversation simulator_).
Pronouns are chosen each time, and avoiding pronouns usually reserved for people (and pets), e.g. _he_, _she_, and singular _they_ is a good first step. But subtle choices—such as grouping algorithms and people under _you_ or _them_—can anthropomorphize. Separating systems from people and avoiding collective pronouns is preferable.
Examples:
**_who’s right?_** → **_is the machine output correct?_**
**_they produce results_** → **_the team uses it [the system] to produce results_**
Biological metaphors
Computer scientists working in "AI" (and its subfields) have been embedding biological metaphors in their technical terminology for a long time. These turns of phrase might have been metaphorical in their origins, but they also function to suggest more similarity than is actually there. When revising away from biological metaphors, ask how system functionality can be more precisely described to give readers a clearer sense of what is actually happening.
Examples:
**_neural networks_** → **_weighted networks_** (from Hunger 2023)
**_the model consumes data_** → **_data is used in setting model weights_**
Reflections
We encourage you to try out the above rephrasings and to create some of your own in the same spirit! It can feel awkward at first, but in our experience it is easier than reliably pronouncing or spelling the word _anthropomorphization_, so there is that.
It can also feel a bit socially awkward, because you are swimming against linguistic and cultural currents, but that can also be rewarding in and of itself. At a talk she gave in January, Emily was asked by a student how to contribute to the resistance against "AI" in conversations with friends, without being a stick in the mud. Emily said: Be a stick in the mud! If you think about our current situation as mired in mud that's hard to walk in, if you plant a stick, you can start to create firm ground for others to join you on.