One thing we can do is help identify the epistemic risks associated with these technological trends. As some have already noted, technologies using AI and opaque algorithms might very well, e.g., perpetuate and accentuate biases against marginalized groups, promote epistemic bubbles and echo chambers, help the spread of toxic misinformation (propaganda, hoaxes, conspiracy theories, “fake” news, “deepfakes”), and produce outputs that lack justification. Some of these risks constitute obstacles to acquiring knowledge or justified beliefs about important matters. Others may constitute or perpetuate various forms of epistemic injustice. Epistemic injustices may, e.g., be present in labeled data sets that are used to train artificial neural networks. These are some of the questions we’d like to discuss at this year’s annual meeting of the Canadian Epistemological Society.