论文标题
伪AI偏见
Pseudo AI Bias
论文作者
论文摘要
伪人工智能偏见（PAIB）在文献中广泛传播，这可能会导致社会上不必要的AI恐惧，加剧了访问和分享AI应用程序利益的持久不平等和差异，并浪费了在AI研究中投资的社会资本。这项研究系统地回顾了文献中的出版物，以介绍以下三种类型的PAIB，这是由于以下原因：a）误解，b）伪机械偏见和c）过度期望。我们讨论了PAIBS的后果和解决方案，包括对用户进行AI应用程序的认证，以减轻AI恐惧，为AI应用程序提供自定义的用户指南以及开发系统的系统方法来监视偏见。我们得出的结论是，由于误解，伪机械偏见以及对算法预测的过度期望引起的PAIB在社会上有害。
Pseudo Artificial Intelligence bias (PAIB) is broadly disseminated in the literature, which can result in unnecessary AI fear in society, exacerbate the enduring inequities and disparities in access to and sharing the benefits of AI applications, and waste social capital invested in AI research. This study systematically reviews publications in the literature to present three types of PAIBs identified due to: a) misunderstandings, b) pseudo mechanical bias, and c) over-expectations. We discussed the consequences of and solutions to PAIBs, including certifying users for AI applications to mitigate AI fears, providing customized user guidance for AI applications, and developing systematic approaches to monitor bias. We concluded that PAIB due to misunderstandings, pseudo mechanical bias, and over-expectations of algorithmic predictions is socially harmful.
