SAYAS BLOG

Savannalee Hodgkinson

In the world we currently live in we are flooded with Temu and Shein advertisements and hauls. Either you are on the side of intense minimalism fiercely rejecting fast fashion or are an enthusiastic explorer of this mass of products. 

Take a second and close your eyes, imagine all the inhabitants in your home closet, your shoes, bags, jackets, hats, all of it, even the forgotten clothes you insist you will one day again fit into. Imagine walking out of your house wearing every single garment you possess. A scarlet letter screaming out your consumption. Where does all our clothing end up when it is thrown away? While our disposed clothing is out of sight, it should not be out of mind. We cannot afford to live in a throw-away culture because the things we have thrown away end up somewhere in our environment polluting it.

The cost of fast fashion is one of its largest attractions. I could purchase a new closet full of clothes from a site like Shein for a fraction of what it would cost at a local store (with the exception of those sneaky import taxes). But is it worth it when fast fashion clothing is made to expire after a measly seven wears? I think for many South Africans, the answer is yes because we cannot pass up the opportunity to wear affordable fashion-forward clothing that dupes trends seen in international Fashion Weeks. However, we cannot ignore the horror stories associated with fast fashion brands, from their brutal impact on the environment to inhumane, racialized, and forced labour practices. Around 75% of clothing ends up in a landfill while it is still in a wearable condition and at the current rate of clothing being made, the fashion industry is contributing to 20% of wastewater and 9% of microplastics found in the ocean every year. With clothing production and consumption at an all-time high, we must consider the sustainability of our fashion choices. 

An Alternative

Enter thrifting, the practice of purchasing and selling second-hand clothing thereby extending the lifespan of clothing that would otherwise end up in a landfill. Thrifting is not a new concept in South Africa, or indeed globally. If you walk through the Johannesburg city centre or any other areas of informal commerce, you would likely see someone selling second-hand clothing in a huge pile on the side of the road. This is at its core, what we call circular fashion that makes our consumption more sustainable. 

Ironically, some individuals are purchasing more clothes than they normally would, knowing that they can sell them second-hand and get a portion of their money back. This kind of practice juxtaposes the concept of sustainable fashion as it focuses on profit-making as opposed to supporting a circular fashion economy where the life of existing clothing is extended.  Thus, there are hidden environmental costs associated with thrifting that one should be aware of if they are thrifting with sustainability in mind. Another consideration would be the environmental footprint associated with the packaging and transportation of single clothing items thrifted online.  Transport is one of the largest contributors to greenhouse gas emissions, and if you are buying a single second-hand clothing item that needs to be packaged (think of the resources that go into packaging) and then transported across the country, that does not scream eco-consciousness.  

Some thrifters are cautious to thrift products that are not associated with fast fashion brands. If this becomes a primary principle of thrifting, does that not mean that such clothing will end up in landfills anyway since they may not be purchased second-hand? Other challenges to thrifting include COVID-19  leaving a lasting impact on the importance of hygiene and many individuals have a perception of second-hand clothing carrying germs, however, all these clothes need is a good wash before wearing them, just as new clothes do. Secondly, purchasing second-hand comes with a fear of being scammed, particularly when you are purchasing from Instagram thrift stores where the clothing you have purchased may arrive in a very different condition to what you were expecting. Thus, credible stores like Yaga and physical stores where people can have a tangible experience with the clothing they want to buy are recommended. 

A final thought

Thinking about the impending doom of our environment can send one into a panic attack and one person alone doing everything perfectly for the planet is not going to save it, but if we all start doing better things with the planet in mind, we can make a change. So next time you want to order something from a fast fashion website, or buy shoes simply because they are on sale, give it a second thought. 

Gabriela Penelopé Carolus

New researchers encounter various tools as they begin their academic journey. You might have already learned about AI tools. Alternatively, you might be overwhelmed by the volume of AI tools and question their utility. These questions can leave you in the dark about which to select or if you should abide by the rumours or fears of not incorporating another tool into your research project. 

Fear not, simply put, Artificial Intelligence (AI) is changing scientific discovery by automating data analysis, hypothesis testing, and pattern recognition in fields such as astronomy, healthcare, materials science, and bioinformatics. Machine learning (ML) and generative adversarial networks are speeding up research and creating new opportunities for innovation. AI techniques in materials characterisation are improving efficiency and accuracy, potentially addressing reproducibility challenges in scientific research. In doing so, it adds expert knowledge to AI models, improving their ability to predict future scientific discoveries and inventions. Data-driven AI/ML innovations can speed up scientific discoveries by dealing with challenges such as high system complexity, large search space, incomplete knowledge, and small datasets.

Given the numerous advantages, let’s delve into the use of a specific tool-Generative Artificial Intelligence (AI). This tool, increasingly embraced by early career researchers, holds significant value and credibility in education and scientific applications. In this blog, I will share my insights on its use in Public Health research and highlight its potential to advance public health innovations.

Value vs reputation

Artificial Intelligence (AI) in scientific research raises valid concerns, including potential errors, ethical issues, and misconduct. The reputation of AI being negative stems from the fact that there are risks associated with AI-generated content, which has the potential to compromise scientific integrity. Furthermore, AI models may convey biases and false information. Therefore, as an early career researcher, it is crucial to integrate AI responsibly to maximise its potential without compromising scientific rigour. While there are concerns about AI’s impact on research, addressing these challenges alongside the exciting possibilities AI brings to one’s research is essential.

Scientific Use case

The value of this tool lies in its versatility. Scientists apply Artificial Intelligence (AI) across a wide range of disciplines, including chemistry, biology, medicine, engineering, and computer science. In the medical field, AI aids in diagnostics, treatment planning, and outcome prediction. Computer scientists use AI for network intrusion detection and game development. Additionally, engineers integrate AI into defence services, smart homes, and autonomous vehicles. These areas utilise various AI techniques, with artificial neural networks being the most common. Other techniques include fuzzy expert systems, evolutionary algorithms, and hybrid intelligent systems. The widespread integration of AI has enhanced efficiency and quality across diverse domains.

AI use in public health

In my field of interest, Public Health, I discovered that AI applications include spatial modelling, risk prediction, disease forecasting, and health diagnosis. Despite challenges like limited infrastructure and ethical considerations, AI can revolutionise African healthcare and research, particularly in disease surveillance, diagnostics, health communication, knowledge translation, health literacy/education and treatment optimisation. The integration into health informatics holds promise for improving public health outcomes across the continent, with implementation varying across regions due to differences in technology adoption.

Ensuring AI accuracy through scientific validation

AI is a powerful tool, but it’s not without its limitations. Ensuring AI accuracy in public health research involves modernising data governance, addressing workforce skills gaps, and setting up strategic partnerships while considering ethical implications. Combining AI with human expertise can improve diagnostic accuracy, but challenges like algorithmic bias and generalizability issues persist. Robust clinical evaluation and regulatory frameworks are essential for evidence generation and patient safety. Scientific validation is crucial, and caution is needed when using AI for diagnostic purposes. As such, I would caution graduate students to recognise these limitations and urge them to communicate the uncertainties of this tool when using AI systems to make decisions. 

AI Learning in my public health research

I am committed to improving my ability in data-driven AI/ML innovations for public health research. As I move forward, I am aware of the ethical implications of AI applications. My aim is to advocate for transparency, reproducibility, and health equity while contributing to the progression of research methodologies for the development of AI-powered applications.

I am establishing strategic partnerships with experts across various fields, such as AI/ML, human-computer interaction, design science, medicine, and software engineering, while also actively engaging with the public. This effort has been instrumental in helping me recognize the capabilities and limitations of working with an emerging tool. As a burgeoning researcher, I am mindful of both AI’s strengths and weaknesses. I am dedicated to embracing collaborative and transdisciplinary approaches through continuous learning to fully leverage the potential of these tools in academic environments.