The Problem of Epistemic Regress in Contemporary Epistemology and The Adequacy of Improved Solution Suggestions One of the main problems of epistemology; whether our beliefs about the world are correct or not, that is to say, it’s the problem of whether we have suitable reasons or not to think that we have the enough knowledge. The value of our beliefs and its epistemic justifiability are being examined through this problem. In this respect, Undoubtedly; one of the greatest problem of contemporary (...) epistemology is the problem of epistemic regress that is a problem of justification. On the other hand the solution suggestions developed for this problem, which is shown as a threat factor for the knowledge in point, don’t seem sufficient. In this article, the problem of epistemic regress which is emerging in the contemporary period will be discussed by examining the perspectives of tradition and contemporary epistemology. Four different and similar basic approaches that arise resulting from the problem of regress solution proposals brought by foundationalism, compatibilism, externalism and internalism will be analyzed. (shrink)

In a recent reply to our article, “What is Interpretability?,” Prasetya argues against our position that artificial neural networks are explainable. It is claimed that our indefeasibility thesis—that adding complexity to an explanation of a phenomenon does not make the phenomenon any less explainable—is false. More precisely, Prasetya argues that unificationist explanations are defeasible to increasing complexity, and thus, we may not be able to provide such explanations of highly complex AI models. The reply highlights an important lacuna in our (...) original paper, the omission of the unificationist account of explanation, and affords us the opportunity to respond. Here, we argue that artificial neural networks are explainable in a way that should satisfy unificationists and that interpretability methods present ways in which ML theories can achieve unification. (shrink)

Van Fraassen explains rejections and asymmetries in science in terms of his contextual theory of explanation in the same way that scientists explain observable phenomena in the world in terms of scientific theories. I object that van Fraassen’s skeptical view regarding inference to the best explanation together with the English view of rationality jointly imply that the contextual theory is not rationally compelling, so van Fraassen and his epistemic colleagues can rationally disbelieve it. Prasetya replies that the truth of the (...) contextual theory coincides with its empirical adequacy, so the contextual theory is compelling. He also replies that the contextual theory is compelling, provided that van Fraassen’s argument for it instantiates a compelling subset of IBE. I argue that Prasetya’s replies either fail or help scientific realism. (shrink)

Van Fraassen does not merely perform Bayesian conditionalization on his pragmatic theory of scientific explanation; he uses inference to the best explanation (IBE) to justify it, contrary to what Prasetya thinks. Without first using IBE, we cannot carry out Bayesian conditionalization, contrary to what van Fraassen thinks. The argument from a bad lot, which van Fraassen constructs to criticize IBE, backfires on both the pragmatic theory and Bayesian conditionalization, pace van Fraassen and Prasetya.