 The need for explainable deep nets is all the more pressing because of the discovery of so-called “adversarial examples” (Goodfellow et al. 2014; Nguyen, Yosinski, &amp; Clune 2015). These come in at least two forms: “perturbed images” which are natural photographs modified very slightly in a way that causes dramatic changes in classification by deep nets even though the difference is imperceptible to humans, and “rubbish images”, which are purportedly meaningless to humans but are classified with high confidence scores by deep nets. Adversarial examples have led some to conclude that whatever understanding the net has of objects must be radically different than that of humans. Adversarial examples exhibit a number of surprising properties: though constructed from a particular training set, they are highly effective at fooling other nets trained on the same task, even nets with different training sets and different architectures. Furthermore, the search for effective countermeasures has led to frustrating failures. It has also been discovered, however, that perturbation methods can create images which fool humans (Elsayed et al. 2018), and human subjects can predict nets’ preferred labels for rubbish images with high accuracy (Z. Zhou &amp; Firestone 2019). Others have noted that the features nets detect in adversarial examples lead to reliable classifications in naturally-occurring data, challenging the idea that the nets’ decisions should be counted as mistaken (Ilyas et al. 2019 [OIR]).  These questions intersect with traditional issues about projectibility and induction, potentially offering new test cases for older philosophical conundrums in epistemology and philosophy of science (N. Goodman 1955; Quine 1969; Harman &amp; Kulkarni 2007).