The impact of artificial intelligence (AI) is significant in almost every industry. As many important decisions are now being automated by various AI applications, fairness is fast becoming a vital concern in AI. Moreover, the related literature and industry press suggest that AI systems are often biased towards gender. Thus, there is a need to better understand the contributing factors behind gender bias in AI, along with the current approaches taken to address it. Therefore in this paper, we aim to contribute to the emerging IS literature on AI by presenting a consolidated picture of the most often discussed contributing factors and approaches taken in relation to gender bias in AI in the multidisciplinary literature. Our findings indicates that the more frequently discussed contributing factors include lack of diversity in both data and developers, programmer bias, and the existing gender bias in society, now amplified through AI. Additionally, our findings indicate the most discussed approaches for addressing gender bias in AI include the implementation of diversity in society and data and fairness in AI development, as well as reducing bias in algorithms. Based on our findings, we indicate some future IS research for the better development of AI systems.
Nadeem, Ayesha; Abedin, Babak; and Marjanovic, Olivera, "Gender Bias in AI: A Review of Contributing Factors and Mitigating Strategies" (2020). ACIS 2020 Proceedings. 27.