Circularity in Fashion, powered by AI
An alternative focus for AI in the fashion industry
We are regularly reminded of the impact of our food, transport, and energy systems on our biodiversity and climate. However, fashion has enormous environmental impacts which must be addressed to mitigate climate change.
The United Nations Environmental Programme and the Ellen Macarthur Foundation show that:
- 10% of annual global carbon emissions come from the fashion industry
- 87% of the total fiber input is incinerated or disposed of in a landfill
- 20% of wastewater worldwide comes from fabric dyeing and treatment
- 93 billion cubic meters of water are used yearly
- 50 billion plastic bottles equivalent in microplastics is released yearly
- Consumers buy 60 percent more than they did in 2000, and keep it half as long
- The average garment is worn 10 times before disposal
Despite the current environmental impact, there are ways to address these issues. According to Mckinsey, 70% of the fashion industry’s emissions come from upstream activities such as materials production, preparation and processing which could be mitigated cheaply.
Artificial intelligence (AI) has been integrated into the fashion industry to help drive sales, with most use cases covering dynamic pricing and excess stock clearing, product recommendations to increase conversion, process automation, and trend detection.The following reviews found on Medium  support such claims. Despite the potential for cheap abatement and the importance of the industry in global warming, AI hasn’t been used to reduce emissions and adopt circularity principles.
In this post, I provide case studies for the use of AI to accelerate the circular economy in the fashion industry, refocusing AI as a powerful tool for climate change mitigation, not just a sales tool. I also share my personal opinion as an AI developer in the fashion industry.
What can AI do to accelerate a truly circular fashion?
In the following, I provide a long but not exhaustive list of use cases where AI can be used to make the industry more sustainable in all stages of value creation, including the potential role of consumer behavior change and policy changes supported by AI.
AI can improve Design and Raw Materials Selection
1) AI can be used to design long lasting garments. We can use product reviews and lab data to predict how long products last, supporting the creation of those with the largest life cycles.
2) AI can be used to create truly unique articles leveraging computer vision, natural language processing, and other techniques to reduce assortment size while staying relevant for the consumers.
3) Instead of taking a cost based approach to design and pricing, we can use estimated perceived value and willingness to pay methods. This can support the creation of valuable and profitable products   .
4) Leverage Reinforcement Learning and other methods to create designs that are easy to repair, recycle or decompose.
AI can help to eliminating excess stock and waste
1) AI can be used to reduce excess stocks via better demand forecasting and stock optimization.
2) AI can support pre-order models with zero stock and agile lead times.
3) AI can make renting and second life channel recommendations better.
4) Computer vision can be used to detect the status of garments for resale and second hand channels.
AI can be used to improve the delivery operations and transport emissions
1) AI can be used to reduce returns by making accurate product recommendations or verifying that the product fits before purchase.
2) AI can be used to find similar replacements that are in the same distribution center so that missing stock does not need to be flown in.
3) Using Green Last Mile AI power logistics to deliver products with renewable energy.
AI can help understand consumer behavior and design appropriate policies
1) AI can help to understand product design or policy interventions that lead to longer use of garments.
2) AI can provide insights into the real and perceived values that drive sustainability clothing choices.
3) AI can test theoretically appealing policies to enable sustainable clothing via durability standards or others.
4) BlockChain technology and AI can be used to scale life cycle management assessments for each product and improve footprint transparency.
5) AI can be used the detect and track green washing claims.
The size of the opportunity is immense, so why haven’t we started using AI to accelerate the circular economy? To finish off, I summarize my own perspective, and that of other industry experts, on the challenges for the adoption of AI in circularity and mitigation.
Challenges and opportunities for AI and the circular economy
As discussed in this article, there are many potential opportunities for AI for fashion to intersect with climate mitigation. However, there are significant challenges to adoption of circular fashion powered by AI.
The first challenge is that the field is underdeveloped both in terms of data availability and AI research. I have identified almost no academic research at the intersection of AI for fashion and climate mitigation. There are currently no open-source datasets that can be used. The extensive use of AI in circular fashion requires good data.
Wide-spread implementation of AI for circular fashion requires gathering large amounts of high quality data. This set of barriers presents an opportunity for academics who are interested in novel research directions and data providers interested in being a trusted source for circular fashion data.
Another challenge is that corporations are often unwilling to stop using trusted, yet inefficient, methods in favor of AI, as in the case of life cycle analysis. Currently, life cycle analysis is a manual, data-intensive process that can be performed for a few hundred products a year. The fashion sector produces over a million new products every season, which is a huge opportunity for the use of AI. However, machine learning-based sustainability claims are considered untrustworthy, and a potential source of greenwashing, preventing them from widespread adoption despite being potentially more accurate than manually input claims.
Corporations must also contend with a changing regulatory environment. European regulatory frameworks will soon require companies to provide detailed reports of the carbon footprints of individual products and may require textile companies to properly treat used textiles. This will put enormous pressure on the data and information systems of fashion companies and provide an opportunity for the implementation of new technologies, like blockchain and AI, to provide scalable and reliable information carbon footprint at the product level.
A final challenge relates to the impact of circular fashion on consumer behavior. Product designs powered by AI could shift consumption towards longer lasting products. However, this may increase consumption, an example of Jevons paradox, in which an action taken to address climate change actually increases emissions. The desire for reduced consumption due to circular fashion is complicated by the need for the fashion companies to be profitable. Further sustainability considerations are required to ensure that as AI makes fashion supply chains leaner and products more affordable and durable, consumers will change their consumption to decrease the climate impact of their fashion.
While these challenges can seem daunting, similar challenges have been overcome in industries such as energy, agriculture, and forestry. In these industries, AI helps reduce overall emissions at scale. The potential benefits of using AI to power circular fashion are enormous and the challenges manageable. The time for circular fashion powered by AI has come.