## 1 Introduction  
The use of artificial intelligence (AI) technology within the fashion and textiles industry is growing, with potential applications often focusing on consumer experience (e.g., virtual try-on and digital avatars) to consumer insights, forecasting, and trend analysis [1]. AI is a powerful tool that brands may employ to drive sales, improve consumer experience, and increase efficiency [2]. However, there is also enormous scope for the use of AI within the industry to drive and develop new sustainability initiatives, divert waste, and re-circulate secondhand goods. The global fashion and textile (F&T) industry is a significant contributor to environmental pollution and degradation, largely driven by the fast fashion model that promotes rapid production and consumption cycles. The exact volume of textile waste this sector generates annually is unknown; 92 million tons is frequently referenced, much of which is either incinerated, exported or ends up in landfills, exacerbating the environmental crisis [3; 4]. Currently, these pathways are challenged as export markets are becoming oversaturated [4], with the volumes of textiles being exported, dumped, and landfilled beginning to exceed capacities [5]. Economically, the premature disposal of clothing represents a loss exceeding USD 400 billion annually, highlighting inefficient resource use and missed recycling opportunities [2]. Socially, developing countries often receive large quantities of second-hand clothing, overwhelming local markets and contributing to waste management challenges and public health issues [4]. Environmentally, the decomposition of synthetic fabrics in landfills releases harmful chemicals, exacerbating pollution and environmental injustice [4]. Addressing these challenges requires innovative approaches that extend the lifecycle of textiles and garments, emphasizing reuse, repair, and recycling infrastructures and technology. A circular economy (CE) framework offers a promising solution, aiming to minimize waste and resource usage by creating closed-loop systems [3]. Given the urgency of the F&T waste crisis, previous studies have undertaken systematic reviews on textile waste and circular economy frameworks. However, the focus has primarily been on a more holistic and critical view of the fashion production system, its complex and global supply chain, and assessing existing technologies for material and energy recovery [5] or recycling [6]. While sorting textile waste is only one of the barriers to transitioning to a CE, it is a large one, as identified by [PERSON] et al. [6] and many others. The circular economy concept aims to redefine growth by decoupling economic activity from the consumption of finite resources and designing waste out of the system. In the context of textile recycling and reuse, CE goals emphasize reducing textile waste by extending the lifecycle of products through recycling, reusing, and repurposing materials. This approach not only conserves resources but also reduces environmental impact and fosters sustainable development. By integrating AI-driven textile sorting solutions, we can enhance the efficiency and accuracy of material recovery processes, making significant strides towards achieving CE goals and promoting a more sustainable textile industry. Within this context, AI has emerged as a powerful tool for assessing garment quality and condition, transforming traditional practices and supporting sustainable initiatives [7]. AI technologies, particularly machine learning (ML), deep learning (DL), and computer vision (CV), have demonstrated significant potential in addressing key challenges in the fashion and textile industry. These technologies enable the automation of garment quality inspections, defect detection, textile sorting, and degradation prediction, offering speed, accuracy, and scalability [8]. For instance, recent studies [9; 10] have shown that AI can significantly enhance the reliability of garment inspections while reducing labor cost.