This talk investigates the following two questions: Q1. Under what conditions do human AI-based beliefs qualify as knowledge? Q2. Do the seemingly crazy errors that AI systems sometimes make pose a threat to human AI-based beliefs qualifying as knowledge? The discussion of Q1 and Q2 is set against the background of a stock of examples of AI errors, including adversarial examples drawn from the large literature on image classifiers and LLMs. Many of these errors strike humans as bizarre or crazy—e.g., LLMs ‘hallucinating’ references or an image classifier correctly classifying an image of a panda but switching the output to ‘gibbon’ after the original image is subjected to a humanly imperceptible manipulation of its pixel structure. The talk brings Q1 and Q2 into connection with mainstream epistemology—more specifically, modal epistemology. The key idea is that, in order for a belief output of a given method to qualify as knowledge in a given world w, the belief must not only be true in w; it must likewise be sufficiently modally robust. The talk discusses the prospects of AI-based knowledge, given modal conditions on knowledge and the wealth of adversarial examples that have surfaced in AI research.
