Pseudo AI Bias
It addresses the problem of misleading AI bias perceptions for researchers and society, but it is incremental as it reviews and categorizes existing issues without introducing new methods or data.
This study systematically reviews literature to identify three types of Pseudo AI Bias (PAIB) caused by misunderstandings, pseudo mechanical bias, and over-expectations, concluding that these biases are socially harmful by fostering unnecessary AI fear, exacerbating inequities, and wasting social capital.
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.