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<p>In a reliabilist epistemology of algorithms, a high frequency of accurate output representations is indicative of the algorithm’s reliability. Recently, Humphreys challenged this assumption, arguing that reliability depends not only on frequency but also on the quality of outputs. Specifically, he contends that radical and egregious misrepresentations have a distinct epistemic impact on our assessment of an algorithm’s reliability, regardless of the frequency of their occurrence. He terms these <i>statistically insignificant but serious errors</i> (SIS-Errors) and maintains that their occurrence warrants revoking our epistemic attitude towards the algorithm’s reliability. This article seeks to defend reliabilist epistemologies of algorithms against the challenge posed by SIS-Errors. To this end, I draw upon <i>computational reliabilism</i> as a foundational framework and articulate epistemological conditions designed to prevent SIS-Errors and thus preserve algorithmic reliability.</p>

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In defense of reliabilist epistemology of algorithms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月11日

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  • Juan M. Durán

摘要

In a reliabilist epistemology of algorithms, a high frequency of accurate output representations is indicative of the algorithm’s reliability. Recently, Humphreys challenged this assumption, arguing that reliability depends not only on frequency but also on the quality of outputs. Specifically, he contends that radical and egregious misrepresentations have a distinct epistemic impact on our assessment of an algorithm’s reliability, regardless of the frequency of their occurrence. He terms these statistically insignificant but serious errors (SIS-Errors) and maintains that their occurrence warrants revoking our epistemic attitude towards the algorithm’s reliability. This article seeks to defend reliabilist epistemologies of algorithms against the challenge posed by SIS-Errors. To this end, I draw upon computational reliabilism as a foundational framework and articulate epistemological conditions designed to prevent SIS-Errors and thus preserve algorithmic reliability.