Herman Cappelen and Josh Dever, "Making AI Intelligible: Philosophical Foundations" (Oxford UP, 2021)
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What puzzle about AI ‘saying’ things motivates the book?
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Welcome to New Books in Language, a channel on the New Books Network. I'm Malcolm Keating. Today we'll be talking to Herman Kapellen and Josh Dever, authors of Making AI Intelligible Philosophical Foundations, published in twenty twenty one by Oxford University Press. Welcome to New Books in Language, Herman and Josh.
Thank you.
Hello. Uh well, let's dive in. Your book focuses on AI or artificial intelligence and how philosophy of language is relevant to understanding and maybe even improving it. So let's start with your book's argument at a high level. What's your book's thesis, first of all, and why did you think it was an important book to write?
So I think rather than give you a thesis, I'd rather give you a puzzle because I think the thing that motivates our book centrally is a puzzle that we wanna address. We've got some suggestions for what to say in response to that puzzle, but a lot of what we want to do is just draw attention to that puzzle and get people talking about it some more. So here's a way of thinking about the puzzle. Increasingly, we're surrounded by various kinds of artificial intelligence computer systems. And these computer systems are meant to answer certain kinds of questions for us, make certain kinds of decisions, give us certain kinds of information. And they're doing this by producing some output. They'll dump some text onto a screen, they'll produce a voice through some kind of uh you know voice software.
And so they're they're producing things that we'd like to take us take as telling us something. But we need to make sure we know what these systems are telling us. And one of the things we want to emphasize is it shouldn't be viewed as straightforward what the system is telling you. So here's a kind of motivating example. Suppose you've got an artificial intelligence system whose job is to look at medical records and try to make a diagnosis of a patient. So you might, for example, give it uh digital images of a certain kind of lesion. And the artificial intelligence was to look at the digital image and do some kind of processing on it and render a verdict. And what we'd like is for the verdict to be that this lesion is a malignant tumor or a benign tumor.
So, you know, we take a photo of a lesion on your body and we give it to this computer and it returns a response saying, you know, outputting the word Benign. Now that looks very reassuring, but of course it's only genuinely reassuring if when the computer outputs the word benign, what it means is that it's a benign tumor. But it's not a straightforward matter that that's what the computer means. So think about how these artificial intelligence systems are typically developed.
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Chapters
8 chapters
1
What puzzle about AI ‘saying’ things motivates the book?
0:00–9:37
2
How do the authors explain why AI outputs need semantic content?
9:37–18:16
3
What is de‑anthropocentrized externalism and why is it needed for AI?
18:16–25:47
4
How do externalist theories like Kripke’s and Burge’s apply to neural nets?
25:47–32:07
5
What is the mental‑file framework and how does it help AI refer to people?
32:07–41:32
6
How does teleosemantics link AI function to meaning?
41:32–51:30
7
How does the ‘Lucy is high‑risk’ example illustrate AI predication?
51:30–1:00:18
8
What future research directions do the authors see for AI and philosophy?
1:00:18–1:10:53
Speakers
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Philippe Huneman, "When Metaphysics Meets Biology: Kantian Approaches to the Concept of An Organism" (Routledge, 2026)
Rivka Weinberg, "The Meaning of It All: Ultimate Meaning, Everyday Meaning, Cosmic Meaning, Death, and Time" (Oxford UP, 2026)
PJ DiPietro, "Sideways Selves Travesti and Jotería, "Struggles Across the Américas" (U Texas Press, 2025)