Algorithmic bias in data-driven innovation in the age of AI |
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| Institution: | 1. Emerging Markets Research Centre (EMaRC), School of Management, Swansea University Bay Campus, Swansea SA1 8EN, Wales, UK;2. Symbiosis Institute of Business Management, Pune & Symbiosis International (Deemed University), Pune, India;1. University of Southern California, Marshall School of Business, United States;2. University of Michigan, Ross School of Business, United States;1. Cyprus Center for Algorithmic Transparency, Open University of Cyprus, Faculty of Pure & Applied Sciences, 33 Yiannou Kranidioti Avenue, 2220 Latsia, Nicosia, Cyprus;2. Transparency in Algorithms Group, CYENS Centre of Excellence, 23 Dimarchias Square, 1016 Nicosia, Cyprus;1. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan;2. Department of Business Administration, Asia University, Taichung 41354, Taiwan |
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| Abstract: | 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. |
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| Keywords: | Algorithmic bias Data driven innovation Data bias Method bias Societal bias |
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