Scholarlink

<p>It has been argued that inductive underdetermination entails that machine learning algorithms must be value-laden. This paper draws from the philosophy of induction to rather highlight the epistemic motivations and justifications that play a role in machine learning algorithm design. The analysis offered indicates that some of the arguments from underdetermination to value-ladenness are too quick, but it also supports their conclusion by indicating how the practical realization of these epistemic considerations inevitably introduces various non-epistemically value-laden judgments, too. The suggestion is that exposing value-ladenness is not inconsistent with, and even profits from, appreciation of the epistemic considerations involved.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Values in machine learning: what follows from underdetermination?

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月22日

查看全文
  • Tom F. Sterkenburg

摘要

It has been argued that inductive underdetermination entails that machine learning algorithms must be value-laden. This paper draws from the philosophy of induction to rather highlight the epistemic motivations and justifications that play a role in machine learning algorithm design. The analysis offered indicates that some of the arguments from underdetermination to value-ladenness are too quick, but it also supports their conclusion by indicating how the practical realization of these epistemic considerations inevitably introduces various non-epistemically value-laden judgments, too. The suggestion is that exposing value-ladenness is not inconsistent with, and even profits from, appreciation of the epistemic considerations involved.