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Computational reliabilism has been recently deployed to justify our reliance and trust in computational technologies such as machine learning methods in artificial intelligence. Roughly, these deployments can be understood as seeking to (a) respond to or circumvent the challenges related to epistemic opacity in computational methods, and in doing so, (b) warrant or justify our beliefs regarding the reliability of computational processes and their results; and hence, (c) to reassure us of the possibility of trust in computational methods, practices and artifacts even if these are insurmountably opaque. This chapter aims to elucidate three major challenges to computational reliabilism that have a bearing on its viability both as a general epistemological framework capable of dealing with the advent of computational methods, and as a pragmatic epistemic resolution to the justification problems related to the adoption of opaque computational methods. These challenges are the following:

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Challenges for Computational Reliabilism in AI and Other Computational Methods

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  • 出版模式：

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  • 发表日期：

    2026年

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  • Ramón Alvarado

摘要

Computational reliabilism has been recently deployed to justify our reliance and trust in computational technologies such as machine learning methods in artificial intelligence. Roughly, these deployments can be understood as seeking to (a) respond to or circumvent the challenges related to epistemic opacity in computational methods, and in doing so, (b) warrant or justify our beliefs regarding the reliability of computational processes and their results; and hence, (c) to reassure us of the possibility of trust in computational methods, practices and artifacts even if these are insurmountably opaque. This chapter aims to elucidate three major challenges to computational reliabilism that have a bearing on its viability both as a general epistemological framework capable of dealing with the advent of computational methods, and as a pragmatic epistemic resolution to the justification problems related to the adoption of opaque computational methods. These challenges are the following: