In the contemporary landscape, the threads of feminism, fashion, and artificial intelligence (AI) weave a complex tapestry reflecting and shaping societal evolution. These domains, though seemingly distinct, share parallel trajectories that highlight shifts in societal norms, roles, and expectations, particularly concerning women's empowerment and visibility. This essay explores these interconnections, revealing how technological advances in AI have coincided with and influenced dynamic changes in both feminism and fashion, ultimately reflecting broader societal transformations in the United States.

Alan Turing, often hailed as the father of AI, not only laid the conceptual groundwork for artificial intelligence but also influenced broader cultural and technological landscapes. His pioneering work, particularly the Turing Machine (1936), demonstrated the potential of machines to perform any algorithmic computation, fundamental to the development of digital computers. This period, while primarily focused on technological innovation, also reflected a society grappling with the aftermath of World War II, where fashion and women's roles were undergoing significant transformation.

As Turing and others laid down the theoretical underpinnings of what would become AI, fashion in the 1940s was also experiencing transformation. Post-war fashion saw a move towards simplicity and functionality due to fabric rationing, which inadvertently echoed the efficiency and utility of early computers. The introduction of Christian Dior's New Look in 1947, which emphasized a return to opulence and femininity with its hourglass silhouette, paralleled the optimism and forward-looking spirit that Turing's innovations inspired in the technological realm.

Turing's seminal paper, Computing Machinery and Intelligence (1950), where he proposed the now-famous Turing Test, sparked widespread debate about the capabilities of machines to mimic human intelligence. This intellectual curiosity mirrored the fashion industry's experimentation with new materials and styles that defined the 1950s, such as synthetic fabrics and bold prints, mirroring the era’s fascination with innovation and the future.

The official birth of AI as a research field in 1956 during the Dartmouth Workshop, where the term "Artificial Intelligence" was coined, marked a pivotal moment in tech history. This era's optimism about technology's potential paralleled significant shifts in fashion, where the late 1960s saw the rise of youth culture fashion like the mod look, which embraced bold colors, geometric patterns, and new synthetic materials, reflecting a society increasingly influenced by innovation and change.

The excitement around early AI developments resonated within the fashion industry, which began to integrate more futuristic designs and materials into its collections. The interplay between these fields highlighted a societal shift towards embracing modernity and looking towards the future, not just in technology but in every aspect of daily life, including clothing.

The end of World War II marked a pivotal shift in American society, with women who had participated extensively in the workforce during the war facing new challenges and opportunities. The seeds of second-wave feminism were sown during this era, responding to the reassertion of traditional roles and the burgeoning demand for greater rights and recognition. This period saw the first major surges in movements that would later define feminist discourse. Fashion in the late 1940s and 1950s, dominated by Christian Dior’s New Look, reintroduced a hyper-feminine silhouette emphasizing hourglass figures—a stark contrast to wartime utility clothing.

This movement symbolized a retreat to traditional gender roles but also set the stage for subsequent rebellion in fashion expressions.

By the 1960s, fashion became a field of contestation and experimentation, reflecting the growing clamor for social changes, with miniskirts and mod silhouettes challenging conventions and reflecting a more liberated youth culture.

Second Wave Feminism’s 1970s brought the rise of the miniskirt

As the feminist movements gained momentum, fashion responded with designs that mirrored these societal shifts. The 1970s brought an era of bold expression through clothing, with unisex apparel and the normalization of trousers for women reflecting the push for gender equality. Fashion icons of the era, like Diane von Furstenberg with her wrap dress, championed designs that combined style with practicality and comfort, empowering women in both their professional and personal lives. During the same period, the field of AI experienced its first major setback, a period known as the "AI Winter." Enthusiasm waned as the lofty promises of the 1960s went unfulfilled, leading to significant cuts in funding. As detailed in the IBM Archives, this period highlighted the limitations of early AI technologies and tempered the initial optimism, setting a more realistic foundation for future advancements.

Entering the 1990s, feminism embraced an even broader spectrum, with third-wave feminism focusing on individual identity, diversity, and global perspectives. This wave, fueled by technological advancements, used the burgeoning internet as a platform to spread its message, challenging traditional narratives and promoting a more inclusive approach to gender equality. Fashion in the 1990s and 2000s saw the integration of technology not only in the design and manufacturing processes but also in the retail experience.

A major turning point came with the launch of Boo.com in 1999, widely considered one of the first online fashion retail platforms. Though it ultimately failed due to its overly ambitious technology and the burst of the dot-com bubble, Boo.com was a pioneer in envisioning how fashion could be sold online. It introduced features that were revolutionary for the time, such as virtual try-ons and 3D views of garments—elements that prefigured today’s virtual fitting rooms and interactive shopping experiences. Despite its commercial downfall, Boo.com set the stage for e-commerce giants like Net-a-Porter (founded in 2000), which successfully combined editorial content with luxury fashion retail, reshaping how consumers engaged with style and storytelling in the digital space.

Online shopping began to reshape the industry in profound ways, from logistics and supply chain innovation to real-time inventory tracking and dynamic pricing. Data analytics emerged as a crucial tool for understanding consumer preferences and predicting fashion trends, often drawing from large-scale data sources such as purchase histories, search queries, and even social media behavior. These developments are further detailed in reports by McKinsey & Company, which highlight how data-driven decision-making became central to competitive advantage in the fashion sector.

This era also marked significant advancements in artificial intelligence, particularly through the rise of machine learning, which provided new methods for data analysis and pattern recognition. Technologies like IBM’s Deep Blue and later Watson demonstrated that AI was no longer confined to academic labs but could be applied to solve complex, real-world problems, including those in fashion. From demand forecasting and inventory optimization to automated customer service and personalized shopping experiences, AI began to permeate every layer of the fashion value chain.

As the internet matured, so did the digital consumer. The expectations around convenience, personalization, and brand engagement shifted dramatically, and fashion companies were increasingly expected to meet users where they were—online, mobile, and informed. In this context, the evolution of e-commerce and early AI applications laid a foundational role in what would become a tech-forward, data-centric fashion ecosystem, setting the stage for the current era of smart retail, virtual influencers, and AI-generated fashion.

The 2010s witnessed a groundbreaking shift in artificial intelligence with the advent of deep learning. This subfield of machine learning, which utilizes deep neural networks, has brought unprecedented advancements in computer vision, speech recognition, and natural language understanding. These technologies, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), revolutionized image recognition and text processing, enabling machines to perform tasks with near-human accuracy. In the world of fashion, this meant transformative changes in how brands engage with customers and manage operations.

For instance, AI-powered tools began to significantly alter the landscape of fashion design and retail. Brands like Stitch Fix and ASOS started utilizing AI to analyze customer data and predict fashion trends, tailoring their inventories and designs to meet rapidly changing consumer preferences with remarkable precision. This use of AI not only optimized supply chains but also personalized shopping experiences, making fashion more accessible and attuned to individual tastes.

The introduction of the Transformer architecture in 2017, with its novel self-attention mechanisms, further propelled AI capabilities, especially in processing and understanding language through models like BERT and GPT. For fashion, this meant enhanced customer service interfaces where chatbots could handle complex queries, provide styling advice, or assist with purchases in a more human-like manner. The ability of these systems to understand and generate language has also enabled more dynamic and engaging marketing strategies, where AI can create compelling product descriptions, blog posts, or social media content, resonating deeply with consumers.

The Transformer’s ability to handle sequential data effectively has improved trend forecasting algorithms. These AI models analyze vast arrays of fashion-related data, from runway shows to social media feeds, predicting future trends with a level of detail and accuracy previously unachievable. This capability allows fashion brands to stay ahead in a highly competitive market, where being on trend can significantly impact sales.

The development of Large Language Models (LLMs) such as GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) has significantly transformed multiple industries, and the fashion world is no exception. Originally designed to enhance natural language understanding and generation, these models have evolved into powerful tools that also intersect with visual and creative domains, particularly through the emergence of multimodal models like DALL·E, CLIP, and more recently, tools like GPT-4 and Sora with image capabilities.

This convergence of language and vision has opened up new avenues for innovation in fashion design, marketing, and consumer engagement. In the realm of design, these AI models have introduced groundbreaking capabilities. Where traditional design workflows might require extensive manual sketching, mood boards, and physical prototyping, AI can now accelerate the process dramatically. Designers can input a simple textual prompt, such as “a futuristic evening gown inspired by jellyfish and 1920s Art Deco,” and the AI can instantly generate a range of visual concepts that align with the description. This not only reduces the time and financial resources needed in the early stages of design ideation but also expands creative possibilities, offering unexpected and unconventional interpretations that a human designer might not have considered.

Beyond individual garments, LLMs combined with generative image models have also been used to create entire virtual fashion collections. These digital lines can be showcased on virtual models, either in online lookbooks or immersive experiences within metaverse environments like Decentraland or The Sandbox. For instance, luxury brands such as Balenciaga and Gucci have already experimented with AI-generated digital clothing that consumers can “wear” virtually on avatars, or even in augmented reality filters for platforms like Instagram and Snapchat. These innovations not only engage tech-savvy audiences but also align with growing sustainability goals by minimizing the need for physical samples and waste.

AI tools are also playing a growing role in personalization. With LLMs' ability to process and understand nuanced language, fashion brands can now offer hyper-personalized styling recommendations based on user input. Imagine a chatbot powered by GPT that understands a user’s aesthetic preferences from a conversation and suggests entire outfits or curates mood boards accordingly. This level of interaction goes beyond the usual algorithmic filtering seen on e-commerce platforms, enabling a more conversational and intuitive shopping experience.

AI’s linguistic understanding also enhances marketing efforts. Brands are leveraging LLMs to generate product descriptions, blog posts, ad copy, and even social media captions that are on-brand, culturally relevant, and tailored to specific demographics. For example, a streetwear label might use GPT to generate edgy, Gen-Z-focused captions with slang and humor, while a luxury house might opt for elegant prose that reflects heritage and sophistication.

The implications of these technologies extend to behind-the-scenes operations as well. AI is being used to forecast trends by analyzing massive datasets—from social media platforms, fashion blogs, and runway shows—to predict which colors, silhouettes, and themes are likely to be in demand in upcoming seasons. Tools like CLIP, which connect text and images, allow researchers and creative directors to explore visual trends based on descriptive keywords, enhancing strategic planning in design and marketing.

As we move further into the age of AI with LLMs becoming more sophisticated, their application across various industries, including fashion, is expanding. The ongoing research and development in AI not only focus on enhancing the capabilities of these models but also aim to address significant challenges such as bias, ethical concerns, and the environmental impact of deploying large-scale AI systems.

In fashion, the integration of AI poses both opportunities and challenges. While AI can drive innovation and efficiency, it also necessitates careful consideration of ethical issues, such as data privacy and the displacement of traditional jobs.

The future of fashion, intertwined with AI, points towards a more personalized, sustainable, and technologically integrated industry where the barriers between physical and digital fashion experiences continue to blur.

In summary, the evolution from Turing's foundational work in AI to today's sophisticated LLMs marks a journey of immense technological advancement that has significantly impacted the fashion industry. As AI continues to develop, its role in shaping the future of fashion remains a potent force, promising new levels of innovation and personalization while challenging the industry to remain mindful of ethical and practical implications.

The intertwined evolution of feminism, fashion, and AI reflects a broader narrative of progress and transformation in American society. Each has influenced and been influenced by the others, with technology both a metaphor and a mechanism for change. As we look to the future, the continued advancement of these fields promises further shifts in how we understand and navigate gender, identity, and human interaction in an increasingly digital world.

A note from Leah:

I used women’s fashion as my main source of inspiration for this paper for my Digital Revolution course at Tulane with Professor Walter Isaacson as a unique way to depict not only how drastically the Digital Revolution has impacted women’s roles in the world, but also how its influence on modern technology, media consumption, and the fashion industry is continuing to develop every single day. Of course, the past one hundred years of gender discrimination and feminism movements have shifted women’s rights, but what is less often talked about is how much the fashion industry has changed due to female creativity, technology, and the development of artificial intelligence, and the major effects fashion can have on one’s gender identity.

Throughout my process, I not only noticed drastic differences in the quality of information produced, but I also input AI outputs of one platform into another to further my theory that a lack of creativity and empathy is what truly differentiates people from robots. AI’s inability to detect emotion causes it to be unable to distinguish the difference between a human’s input and something else, but I achieved much better results when describing specific events that occurred during the digital revolution to tailor my outputs. My main goal here is to express a unique perspective on the fashion industry using AI as not only a tool of production, but as an ulterior form of explanation for fashion’s change throughout the mid-late twentieth century. It is also an essay in which I have discovered that AI and fashion’s connection is what will build a sustainable future for the industries, yet there is still so much to address regarding the true sustainable nature of their bond and their impacts on global warming.

An explanation of the assignment:

Your research paper should be at least 3,000 words. You should use at least two artificial intelligence systems (such as Open AI, Google Gemini, Anthropic, Grok, DeepSeek) and begin with this prompt: “Describe the development of AI from Alan Turing to LLMs.” You should prompt, guide, polish, check, add to, and revise the paper using your own ideas and those of the AI tools. The result should be a collaboration between you and the AI tools. It should explore both the history and the engineering technology involved in AI.

The final submission should be in two parts. You will be graded equally on both parts.

Part 1 should describe the processes (at least 7 or 8 steps) and prompts, and methods you used. This should include any mistakes and hallucinations you corrected, how you checked the accuracy of the sources, and how you guided the process to produce an imaginative, informative, and creative paper. Using more than one AI system is strongly encouraged!

Your paper should also include sources; these should be provided by the LLM (and verified by you) or you can find sources to support the statements themselves.

Group work is allowed in teams of up to 3.

Threaded Futures: An Essay on AI, Feminism, and the Fashion Revolution

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