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.

中文翻译：

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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，由于其成功条件的特殊性，当前在机器学习中操作错误信息概念的方法通常存在问题。错误信息的内在价值负载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，这些模型的构造合法性和构造有效性都需要通过比以前认识到的更民主的标准来评估。