Over What Range Should Reliabilists Measure Reliability?
Process reliabilist accounts claim that a belief is justified when it is the result of a reliable belief-forming process. Yet over what range of possible token processes is this reliability calculated? I argue against the idea that all possible token processes (in the actual world, or some other sub...
| Published in: | Erkenntnis Vol. 89; no. 7; pp. 2641 - 2662 |
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| Format: | Article |
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Springer Nature
Oct2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=179636407&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 179636407 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 01650106 5KZ jtl: Erkenntnis issn: 01650106 maglogo: N pubinfo: dt: Oct2024 vid: 89 iid: 7 pid: 237 pub: Springer Nature artinfo: ui: 179636407 10.1007/s10670-022-00645-4 ppf: 2641 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P size: 719KB tig: atl: Over What Range Should Reliabilists Measure Reliability? aug: au: Buijsman, Stefan affil: https://ror.org/02e2c7k09 TU Delft, Jaffalaan 5, 2628 BX, Delft, The Netherlands su: Measurement sug: subj: Measurement ab: Process reliabilist accounts claim that a belief is justified when it is the result of a reliable belief-forming process. Yet over what range of possible token processes is this reliability calculated? I argue against the idea that all possible token processes (in the actual world, or some other subset of possible worlds) are to be considered using the case of a user acquiring beliefs based on the output of an AI system, which is typically reliable for a substantial local range but unreliable when all possible inputs are considered. I show that existing solutions to the generality problem imply that these cases cannot be solved by a more fine-grained typing of the belief-forming process. Instead, I suggest that reliability is evaluated over a range restricted by the content of the actual belief and by the similarity of the input to the actual input. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Erkenntnis is a copyright of Springer, 2024. All Rights Reserved. item: Erkenntnis holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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