Pseudo AI Bias

Abstract

Pseudo Artificial Intelligence bias (PAIB) is broadly discussed in the literature, which can result in unnecessary AI fear in society, thereby exacerbating the enduring inequities and disparities in accessing and sharing the benefits of AI applications and wasting 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.

Keywords: Artificial Intelligence, Bias, AI Bias, Machine Learning