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By Rehana Rutti

When I first began exploring AI, I was not guided by formal training or expert credentials. I am self-taught, curious, and sometimes stubborn. This journey has allowed me to encounter AI on my own terms, filled with excitement, confusion, frustration, and wonder. It quickly became clear that AI is not merely a new tool to learn; it is a powerful force reshaping how we live, connect, and understand the world around us.

I view AI as my ally, something to collaborate with, challenge, and learn from. Yet, I am acutely aware of the significant questions AI raises, along with the shadows it casts on society, the environment, and ethics. These issues are not abstract; they are real and urgent.

The digital divide: Who gets left behind?

One of my foremost concerns is the widening gap between those who have access to AI’s best tools and those who do not. Wealthy individuals and corporations gain timely access to innovative models, rapid data connections, and powerful computing resources. Meanwhile, many people around the globe struggle with slow internet, limited devices, or no access at all. This digital divide is not merely a technological issue, it exacerbates existing inequalities between the Global North and South, urban and rural, rich and poor.

I often reflect on the undersea cables powering global data flows, these invisible lifelines that enable AI, while also considering their environmental toll and the power dynamics involved. AI infrastructure is built and maintained by a handful of powerful players, and the environmental costs are substantial.​ These data centres demand enormous electricity and water to stay operational—hidden costs that quietly accelerate climate change and deepen our dependence on extractive systems.   

Ethics, bias, and the need for accountability

AI models learn from data collected across the internet and society. However, this data carries human biases such as racism, sexism, and economic prejudice, which AI can absorb and amplify if we are not vigilant. I have witnessed how AI can produce unfair or harmful outputs, reinforcing stereotypes instead of challenging them.

The question I grapple with daily is how we build AI systems that serve everyone fairly. Transparency is crucial: understanding what data feeds these models, who is responsible for decisions, and how technology is governed. Voices like Geoffrey Hinton, one of AI’s pioneers, have cautioned against the risks of rapid, unchecked AI development without proper oversight.

AI deception: Deepfakes, hallucinations, and trust

AI’s ability to generate realistic text, images, and videos creates remarkable opportunities but also poses significant risks. Deepfakes can convincingly mimic real people to spread misinformation or commit fraud. AI hallucinations produce false information with confidence, making it increasingly difficult to discern what is true.

This reality makes fact checking more essential than ever. I have had to become my own fact checker, questioning AI’s responses and consulting multiple sources to verify the information I receive. Universities and companies continue to grapple with AI misuse, as even advanced tools like Turnitin​, now equipped with AI detectors—struggle to reliably identify nuanced or lightly edited AI-generated content. 

Environmental impact: The hidden cost

Training large AI models is energy intensive. Every query I make to an AI platform is supported by complex computations running in massive data centres that consume enormous amounts of electricity and water. The sustainability of AI is a pressing concern that I hold dear. Climate change is the defining challenge of our time, and any technology that accelerates energy consumption must be scrutinised.

I advocate for more research and investment in green AI, models that require less energy, utilise renewable power, and reduce environmental harm. We cannot build the future on the backs of a depleted planet.

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