Anupreet Bhui

Abstract This chapter examines the role of Artificial Intelligence–driven trend forecasting in the fast fashion industry, focusing on the ethical and cultural implications of AI’s influence on global fashion trends. AI-powered recommendation systems are central to personalising consumer experiences and forecasting fashion trends. However, their reliance on Eurocentric training data perpetuates Western-centric biases that marginalise non-Western fashion identities. This systemic bias homogenises cultural expressions and suppresses diverse sartorial traditions, raising significant epistemological and ethical concerns. AI-driven forecasting mechanisms often amplify power asymmetries by prioritising commercial viability over cultural plurality. Western-centric datasets and algorithmic biases systematically reinforce dominant aesthetic paradigms, constraining alternative fashion narratives and exacerbating structural inequities. Such exclusionary practices deepen ethical issues surrounding cultural appropriation and intellectual property misappropriation. The chapter concludes with a set of principles for embedding cultural sensitivity into AI architectures, emphasising the inclusion of diverse and representative datasets, fostering equitable partnerships with local fashion communities, and establishing robust ethical guidelines for AI implementation. By adopting a critical, interdisciplinary approach, these principles aim to advance scholarly discourse on AI’s role in shaping global fashion narratives and the development of a more equitable, culturally responsive, and representative paradigm for AI-powered trend forecasting in the fast fashion industry.

This chapter examines the role of Artificial Intelligence–driven trend forecasting in the fast fashion industry, focusing on the ethical and cultural implications of AI’s influence on global fashion trends. AI-powered recommendation systems are central to personalising consumer experiences and forecasting fashion trends. However, their reliance on Eurocentric training data perpetuates Western-centric biases that marginalise non-Western fashion identities. This systemic bias homogenises cultural expressions and suppresses diverse sartorial traditions, raising significant epistemological and ethical concerns. AI-driven forecasting mechanisms often amplify power asymmetries by prioritising commercial viability over cultural plurality. Western-centric datasets and algorithmic biases systematically reinforce dominant aesthetic paradigms, constraining alternative fashion narratives and exacerbating structural inequities. Such exclusionary practices deepen ethical issues surrounding cultural appropriation and intellectual property misappropriation. The chapter concludes with a set of principles for embedding cultural sensitivity into AI architectures, emphasising the inclusion of diverse and representative datasets, fostering equitable partnerships with local fashion communities, and establishing robust ethical guidelines for AI implementation. By adopting a critical, interdisciplinary approach, these principles aim to advance scholarly discourse on AI’s role in shaping global fashion narratives and the development of a more equitable, culturally responsive, and representative paradigm for AI-powered trend forecasting in the fast fashion industry.