When I moved to the Pacific Northwest I was surprised how much people volunteered to me that they loved the Southern word “y’all.” It’s a great inclusive way to call a group together or refer to a team. I love it, too. But my favorite Southern phrase is “might could.” It’s double-hedged, which may appear to be redundant or imprecise, but actually it’s the opposite. It’s a finely calibrated expression of a qualified possibility that a single modal can’t quite capture. “Could” alone is too open. “Might” alone is too tentative. “Might could” lands somewhere specific that neither word reaches on its own. It’s also situated. You know something about the speaker when they say it. It carries place, community, a whole set of social relations. Which is exactly what Haraway is talking about in situated knowledge._
Hedging language can be perceived as negative or as an indication that the speaker isn’t confident. But in academic circles it often is interpreted as a signal of some epistemic humility or recognition that the concept has enough complexity that you need a bit of hedging to remain accurate. When a scientist says “probably,” a doctor says “likely,” a colleague says “I’m fairly certain,” those words are doing the real epistemic work of communicating a speaker’s actual relationship to uncertainty, calibrated by experience, context, and stakes. It’s worth reflecting on what is lost if these turns of phrase are stripped of their nuance.
When I read ‘Probably’ Doesn’t Mean the Same Thing to Your AI as it Does to You, I was struck that our LLMs may not be using hedging language in the way that we do. LLMs use words like “probably,” “likely,” and “almost certain” inconsistently, averaging over conflicting usages in training data rather than assessing actual odds. The article also points to an interesting intersection with gender studies, showing that the same probability expressed differently depending on whether the prompt says “he” or “she.”
This is a really specific kind of epistemic failure, and an interesting one! Hedging language is how knowledge communities signal the limits of what they know. Strip that calibration out and you get fluency that performs humility while enacting the view from nowhere. This is Haraway’s god trick at the lexical level. We’re moving beyond the synthesis of sources and into in individual word choices.
We’ve all seen use cases in which AI in increasingly being used to summarize research, brief decision-makers, and mediate information. We also are all aware of the conflicting views on to what extent that information is actually good. For now, at least, it seems that we may also have to consider the word choice itself. When the methods we have to convey certainty lose their clarity we may find ourselves being overconfident in our interpretation of words, only to find we’ve made decisions without the information we assumed was supporting our path. Things appear as they were, but in reality the world shifted around us. We read “probably” and think we know how confident to be, but the word has already lost its weight.
This is a post in an ongoing project exploring libraries, knowledge, and the epistemic stakes of artificial intelligence. I’m drawing on social epistemology, feminist theory, and two decades of practice in academic libraries.