The fashion industry could not exist without storytelling. Compelling and aspirational stories conveyed through catwalks, campaigns and social media are the stuff that make garments fashionable, fostering a strong desire to be seen wearing them.

Fashion’s stories can spread positive messaging about issues that affect us all. In 2020, Stella McCartney’s Paris show featured models wearing cartoonish animal costumes. This humorous stunt emphasised a serious point about the “planet-friendly” brand’s pledge not to use leather, fur, skins, feathers or animal glues.

But more often, the darker, more unpalatable truth is that fashion’s storytelling drives overconsumption. And it defines unrealistic beauty expectations that exclude many by perpetuating western standards about what is normal and acceptable.

As a cultural historian who researches fashion, I believe the industry has to do better to effect change, and this can be achieved through stronger, more inclusive and responsible storytelling.

Fashion and world problems

According to recent fashion industry reports, storytelling is becoming more prominent as brands seek to demonstrate their social responsibility by forging deeper relationships with consumers. The increased significance of storytelling within fashion can be linked to two themes that have defined social and political debate about the world’s post-COVID recovery: self and society.

Consumers want more meaningful experiences that enable them to explore their identities and connect with others. Fashion is the ideal medium for this, especially during a time of social and political unease. The industry’s global reach means that visual cues and messaging conveyed through clothing campaigns can be easily shared and understood.

The Business of Fashion’s report, The State of Fashion 2024, links the increased importance of storytelling to consumers being “more demanding when it comes to authenticity and relatability”. People want to buy brands that share and support their values.

The consumer group most concerned to align their lifestyle choices and beliefs with the companies that clothe them is Gen-Z – people born between 1996 and 2010 – who “value pursuing their own unique identities and appreciate diversity”.

The increasing prominence of storytelling in fashion is also linked to the industry’s global sway and corresponding social responsibility. Organisations like the UN are increasingly clear that the fashion industry will only help tackle the global challenges emphasised by COVID if it uses its influence to change consumers’ mindsets.

The uneven social impact of the pandemic, which emphasised longstanding inequalities, provided a wake-up call to take action on many global problems, including climate change, overconsumption and racial discrimination. This makes the fashion industry, which contributes 2% to global GDP, a culprit but also a potential champion for driving change.

The British Fashion Council’s Fashion Diversity Equality & Inclusion Report, published in January 2024, highlights “fashion’s colossal power to influence, to provide cultural reference and guide social trends”. Similarly, the UN’s Fashion Communication Playbook, published last year, urges the industry to use its “cultural reach, powers of persuasion and educational role to both raise awareness and drive a shift towards a more sustainable and equitable industry”.

To do this, the UN’s report urges storytellers, imagemakers and role models to change the narrative of the fashion industry. They are asked to educate consumers and inspire them to alter their behaviour if it can help create positive change.

Fashion’s new stories

Since the pandemic, there is evidence the fashion industry has begun to change the content and form of the stories it tells, chiefly by putting a human face on current global challenges. Large-scale, entrenched social problems are being explored through real-life stories. This can help people to understand the problems that confront them, and grasp their role in working towards overcoming them.

One example is Nike’s Move to Zero campaign, a global sustainability initiative which launched during the pandemic in 2020. Instead of endless statistics and apocalyptic warnings about crisis-point climate emergency, Nike encourages people to “refresh” sports gear with maintenance and repair. Old Nike products that have been recreated by designers are sold through pop-ups. When salvage is not possible, Nike provides ways for people to recycle and donate old products.

By encouraging relatively small changes that align the lifecycle of a product with consumers’ everyday lives, Nike’s campaign challenges the traditional idea of clothes being new, immediate and ultimately disposable by making change aspirational.

Narrative hang-ups

While some fashion brands are rethinking the stories they tell, my recent book, Hang-Ups: Reflections on the Causes and Consequences of Fashion’s Western Centrism, explains that some of fashion’s most powerful and harmful stories are deep-rooted.

Concepts defined during the 18th and 19th centuries – civilisation, anthropology, sexology – still influence how the fashion industry engages with age, gender, race and sex. Its drive for newness and the way it pushes the idea that purchasing expensive brands brings automatic status is also based on traditional western social values that fit poorly with 21st-century perspectives and priorities.

The persistence of centuries-old attitudes is apparent too in Nike’s Move to Zero campaign, however well-intentioned. While the initiative is clearly conceived to influence consumer behaviour in a positive way, it still doesn’t fundamentally address what the fashion industry is and does. But at the very least, it accepts that fashion functions through high consumption and the sense of status that owning and wearing a brand confers.

Throwing everything out

One of the key points I make in my book is that effective change will be more likely if we understand how the industry developed into what it is today. This calls for more audacious storytelling that critiques notions of normality, acceptability and inclusivity.

One example is Swedish brand Avavav, which commits itself to “creative freedom driven by humour, entertainment and design evolution”. In February 2024, the brand’s Milan catwalk show concluded with models being pelted with litter. This experimental performance explored prevailing social media stories by calling out online trolls and highlighting the hurt of hate speech, within and beyond the fashion industry.

Naturally, it caused a sensation and was widely covered in the media. A stunt perhaps, but it got people talking and drew attention to designer Beate Karlsson’s message about online hate. Clearly, compelling and innovative storytelling has the power to change minds and behaviour.

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Benjamin Wild does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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Tuberculosis is the world’s deadliest bacterial infection. It afflicted over 10 million people and took 1.3 million lives in 2022. These numbers are predicted to increase dramatically because of the spread of multidrug-resistant TB.

Why does one TB patient recover from the infection while another succumbs? And why does one drug work in one patient but not another, even if they have the same disease?

People have been battling TB for millennia. For example, researchers have found Egyptian mummies from 2400 BCE that show signs of TB. While TB infections occur worldwide, the countries with the highest number of multidrug-resistant TB cases are Ukraine, Moldova, Belarus and Russia.

Researchers predict that the ongoing war in Ukraine will result in an increase in multidrug-resistant TB cases because of health care disruptions. Additionally, the COVID-19 pandemic reduced access to TB diagnosis and treatment, reversing decades of progress worldwide.

Rapidly and holistically analyzing available medical data can help optimize treatments for each patient and reduce drug resistance. In our recently published research, my team and I describe a new AI tool we developed that uses worldwide patient data to guide more personalized and effective treatment of TB.

Predicting success or failure

My team and I wanted to identify what variables can predict how a patient responds to TB treatment. So we analyzed more than 200 types of clinical test results, medical imaging and drug prescriptions from over 5,000 TB patients in 10 countries. We examined demographic information such as age and gender, prior treatment history and whether patients had other conditions. Finally, we also analyzed data on various TB strains, such as what drugs the pathogen is resistant to and what genetic mutations the pathogen had.

Looking at enormous datasets like these can be overwhelming. Even most existing AI tools have had difficulty analyzing large datasets. Prior studies using AI have focused on a single data type – such as imaging or age alone – and had limited success predicting TB treatment outcomes.

We used an approach to AI that allowed us to analyze a large and diverse number of variables simultaneously and identify their relationship to TB outcomes. Our AI model was transparent, meaning we can see through its inner workings to identify the most meaningful clinical features. It was also multimodal, meaning it could interpret different types of data at the same time.

Once we trained our AI model on the dataset, we found that it could predict treatment prognosis with 83% accuracy on newer, unseen patient data and outperform existing AI models. In other words, we could feed a new patient’s information into the model and the AI would determine whether a specific type of treatment will either succeed or fail.

We observed that clinical features related to nutrition, particularly lower BMI, are associated with treatment failure. This supports the use of interventions to improve nourishment, as TB is typically more prevalent in undernourished populations.

We also found that certain drug combinations worked better in patients with certain types of drug-resistant infections but not others, leading to treatment failure. Combining drugs that are synergistic, meaning they enhance each other’s potency in the lab, could result in better outcomes. Given the complex environment in the body compared with conditions in the lab, it has so far been unclear whether synergistic relationships between drugs in the lab hold up in the clinic. Our results suggest that using AI to weed out antagonistic drugs, or drugs that inhibit or counteract each other, early in the drug discovery process can avoid treatment failures down the line.

Ending TB with the help of AI

Our findings may help researchers and clinicians meet the World Health Organization’s goal to end TB by 2035, by highlighting the relative importance of different types of clinical data. This can help prioritize public health efforts to mitigate TB.

While the performance of our AI tool is promising, it isn’t perfect in every case, and more training is needed before it can be used in the clinic. Demographic diversity can be high within a country and may even vary between hospitals. We are working to make this tool more generalizable across regions.

Our goal is to eventually tailor our AI model to identify drug regimens suitable for individuals with certain conditions. Instead of a one-size-fits-all treatment approach, we hope that studying multiple types of data can help physicians personalize treatments for each patient to provide the best outcomes.

Sriram Chandrasekaran receives funding from the US National Institutes of Health.

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