期刊
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
卷 60, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ijinfomgt.2021.102387
关键词
Algorithmic bias; Data driven innovation; Data bias; Method bias; Societal bias
Data-driven innovation has the potential to transform innovation in the age of AI, but algorithmic biases may lead to unjust data product developments. The study identifies data bias, method bias, and societal bias as the three major sources of algorithmic bias.
Data-driven innovation (DDI) gains its prominence due to its potential to transform innovation in the age of AI. Digital giants Amazon, Alibaba, Google, Apple, and Facebook, enjoy sustainable competitive advantages from DDI. However, little is known about algorithmic biases that may present in the DDI process, and result in unjust, unfair, or prejudicial data product developments. Thus, this guest editorial aims to explore the sources of algorithmic biases across the DDI process using a systematic literature review, thematic analysis and a case study on the Robo-Debt scheme in Australia. The findings show that there are three major sources of algorithmic bias: data bias, method bias and societal bias. Theoretically, the findings of our study illuminate the role of the dynamic managerial capability to address various biases. Practically, we provide guidelines on addressing algorithmic biases focusing on data, method and managerial capabilities.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据