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  1. Grounding, Understanding, and Explanation.Wes Siscoe - 2022 - Pacific Philosophical Quarterly 103 (4):791-815.
    Starting with the slogan that understanding is a ‘knowledge of causes’, Stephen Grimm and John Greco have argued that understanding comes from a knowledge of dependence relations. Grounding is the trendiest dependence relation on the market, and if Grimm and Greco are correct, then instances of grounding should also give rise to understanding. In this paper, I will show that this prediction is correct – grounding does indeed generate understanding in just the way that Grimm and Greco anticipate. However, grounding (...)
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  • The Epistemic Value of Understanding-why.Xingming Hu - 2023 - Episteme 20 (1):125-141.
    Some philosophers (e.g., Pritchard, Grimm, and Hills) recently have objected that veritism cannot explain the epistemic value of understanding-why. And they have proposed two anti-veritist accounts. In this paper, I first introduce their objection and argue that it fails. Next, I consider a strengthened version of their objection and argue that it also fails. After that, I suggest a new veritist account: Understanding-why entails believing the truth that what is grasped is accurate (or accurate enough), and it is this true (...)
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  • In what sense is understanding an intellectual virtue?Xingming Hu - 2019 - Synthese 198 (6):5883-5895.
    In this paper, I distinguish between two senses of “understanding”: understanding as an epistemic good and understanding as a character trait or a distinctive power of the mind. I argue that understanding as a character trait or a distinctive power of the mind is an intellectual virtue while understanding as an epistemic good is not. Finally, I show how the distinction can help us better appreciate Aristotle’s account of intellectual virtue.
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  • How Values Shape the Machine Learning Opacity Problem.Emily Sullivan - 2022 - In Insa Lawler, Kareem Khalifa & Elay Shech (eds.), Scientific Understanding and Representation. Routledge. pp. 306-322.
    One of the main worries with machine learning model opacity is that we cannot know enough about how the model works to fully understand the decisions they make. But how much is model opacity really a problem? This chapter argues that the problem of machine learning model opacity is entangled with non-epistemic values. The chapter considers three different stages of the machine learning modeling process that corresponds to understanding phenomena: (i) model acceptance and linking the model to the phenomenon, (ii) (...)
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