Machine learning, misinformation, and citizen science

IF 1.5 1区哲学 Q1 HISTORY & PHILOSOPHY OF SCIENCE

European Journal for Philosophy of Science Pub Date : 2023-11-22 DOI:10.1007/s13194-023-00558-1

Adrian K. Yee

引用次数: 0

Abstract

Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens’ and social scientists’ concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.

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机器学习、错误信息和公民科学

与自然科学和社会科学中使用的其他模型相比，机器学习中操作错误信息概念的当前方法通常存在问题，因为它们的成功条件具有特殊性。错误信息的内在价值负荷以及公民和社会科学家的错误信息概念之间的动态关系共同表明，这些模型的结构合法性和结构有效性都需要通过比以前认识到的更民主的标准来评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

European Journal for Philosophy of Science HISTORY & PHILOSOPHY OF SCIENCE-

CiteScore

2.60

自引率

13.30%

发文量

期刊介绍： The European Journal for Philosophy of Science publishes groundbreaking works that can deepen understanding of the concepts and methods of the sciences, as they explore increasingly many facets of the world we live in. It is of direct interest to philosophers of science coming from different perspectives, as well as scientists, citizens and policymakers. The journal is interested in articles from all traditions and all backgrounds, as long as they engage with the sciences in a constructive, and critical, way. The journal represents the various longstanding European philosophical traditions engaging with the sciences, but welcomes articles from every part of the world.