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  1. Two Arguments for Evidentialism.Jonathan Way - 2016 - Philosophical Quarterly 66 (265):805-818.
    Evidentialism is the thesis that all reasons to believe p are evidence for p. Pragmatists hold that pragmatic considerations – incentives for believing – can also be reasons to believe. Nishi Shah, Thomas Kelly and others have argued for evidentialism on the grounds that incentives for belief fail a ‘reasoning constraint’ on reasons: roughly, reasons must be considerations we can reason from, but we cannot reason from incentives to belief. In the first half of the paper, I show that this (...)
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  • Practical Reasoning.Antti Kauppinen - 2018 - In Daniel Star (ed.), The Oxford Handbook of Reasons and Normativity. Oxford University Press.
    This chapter presents two contemporary pictures of practical reasoning. According to the Rule-Guidance Conception, roughly, practical reasoning is a rule-guided operation of acquiring (or retaining or giving up) intentions so as to meet synchronic requirements of rationality. According to the Reasons-Responsiveness Conception, practical reasoning is a process of responding to reasons we take ourselves to have, and its standards of correctness derive from what we objectively have reason to do, if things are as we suppose them to be. I argue (...)
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  • Reasoning with Reasons.Daniel Star - forthcoming - In Conor McHugh, Jonathan Way & Daniel Whiting (eds.), Normativity: Epistemic and Practical. Oxford: Oxford University Press. pp. 241-59.
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  • What is Reasoning?Conor McHugh & Jonathan Way - 2018 - Mind 127 (505):167-196.
    Reasoning is a certain kind of attitude-revision. What kind? The aim of this paper is to introduce and defend a new answer to this question, based on the idea that reasoning is a goodness-fixing kind. Our central claim is that reasoning is a functional kind: it has a constitutive point or aim that fixes the standards for good reasoning. We claim, further, that this aim is to get fitting attitudes. We start by considering recent accounts of reasoning due to Ralph (...)
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  • Against the Taking Condition.Conor McHugh & Jonathan Way - 2016 - Philosophical Issues 26 (1):314-331.
    According to Paul Boghossian and others, inference is subject to the taking condition: it necessarily involves the thinker taking his premises to support his conclusion, and drawing the conclusion because of that fact. Boghossian argues that this condition vindicates the idea that inference is an expression of agency, and that it has several other important implications too. However, we argue in this paper that the taking condition should be rejected. The condition gives rise to several serious prima facie problems and (...)
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  • How Reasoning Aims at Truth.David Horst - forthcoming - Noûs.
    Many hold that theoretical reasoning aims at truth. In this paper, I ask what it is for reasoning to be thus aim-directed. Standard answers to this question explain reasoning’s aim-directedness in terms of intentions, dispositions, or rule-following. I argue that, while these views contain important insights, they are not satisfactory. As an alternative, I introduce and defend a novel account: reasoning aims at truth in virtue of being the exercise of a distinctive kind of cognitive power, one that, unlike ordinary (...)
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  • The Self-Effacement Gambit.Jack Woods - 2019 - Res Philosophica 96 (2):113-139.
    Philosophical arguments usually are and nearly always should be abductive. Across many areas, philosophers are starting to recognize that often the best we can do in theorizing some phenomena is put forward our best overall account of it, warts and all. This is especially true in esoteric areas like logic, aesthetics, mathematics, and morality where the data to be explained is often based in our stubborn intuitions. -/- While this methodological shift is welcome, it's not without problems. Abductive arguments involve (...)
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  • The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity.Ayanna Howard & Jason Borenstein - 2018 - Science and Engineering Ethics 24 (5):1521-1536.
    Recently, there has been an upsurge of attention focused on bias and its impact on specialized artificial intelligence applications. Allegations of racism and sexism have permeated the conversation as stories surface about search engines delivering job postings for well-paying technical jobs to men and not women, or providing arrest mugshots when keywords such as “black teenagers” are entered. Learning algorithms are evolving; they are often created from parsing through large datasets of online information while having truth labels bestowed on them (...)
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