DEV Community: Muhammad Abdiel Al Hafiz

The Gen Z Privilege And The Blind Spot in AI Era

Muhammad Abdiel Al Hafiz — Tue, 25 Nov 2025 10:24:53 +0000

Late 2022. I still remember the excitement. I pulled my classmate aside, opened my laptop, and typed a prompt into this new thing called ChatGPT. When the text streamed back, eyes widened. It felt like magic. It felt like the future.

Back then, my message to everyone was simple

"You guys need to try this. It's cool. It's going to change everything."

Fast forward to Saturday, November 1 (Late 2025). I was standing in front of 70+ people at the Soedirman Digital School event with Purwokerto Dev. I was still talking about AI. But the message had changed completely.

I wasn't there to hype the tools anymore. I was there to talk about the risks. The bias. The cognitive gap.

How did I go from a "fanboy" to a "realist"? Here is the story.

The Gen Z Privilege

We need to admit something, as Gen Z, we have a massive privilege. We adapt fast. New AI tool released? We figure it out in 10 minutes. Deepfake video on timeline? We spot the glitchy eyes in seconds.

But this privilege creates a blind spot. We often forget that not everyone sees what we see.

In the talk, I highlighted a harsh reality. While we are busy debating which LLM is faster, the older generation is struggling to distinguish between a real video call and an AI-generated scam.

Mind The Cognitive Gap

The gap between "Knowing AI exists" and "Understanding how AI works" is where the danger lies.

I call this the Cognitive Gap. Bad actors are exploiting this gap ruthlessly. From online gambling (judi online) disguised with algos, to voice cloning scams targeting parents. It's not just about financial loss anymore, in some cases, lives are at stake.

For us developers, an error is a bug in the code. For non-techies, an "error" in trusting AI can mean losing their life savings.

Our Unwritten Duty

So, what is the solution? Stop building AI? No. I am still an AI Engineer. I still code. I still build workflows. But I realized that those of us who understand the code have an unwritten obligation.

We don't need to host a grand seminar or become a keynote speaker to make a difference. The most impactful thing we can do is often the simplest: Go home and talk to your parents.

Talk to your parents and non tech friends. Explain to them:

"Mom, if you see a video of a politician speaking perfect Mandarin, check the source first." "Luke, if I call you asking for money but my voice sounds flat, call me back immediately."

We need to evolve from being just "Tech Support" who fixes the WiFi, to becoming "Ethical Guides" who help them navigate this synthetic reality.

The Compounding Effect

Imagine if every Gen Z developer or just everyone in the photo educated just one family member or one friend. It creates a compounding effect. One family educated, one friend aware.

If millions of us do this, the "Cognitive Gap" shrinks. Society becomes immune to the cheap tricks of AI scammers.

Three years ago, I thought my job was to tell people how smart AI is. Today, I realize my job is to tell people where AI fails, so they don't get hurt by it.

How Grad-CAM Helped Me Explain Alzheimer’s Predictions

Muhammad Abdiel Al Hafiz — Wed, 27 Aug 2025 08:21:35 +0000

When we talk about Artificial Intelligence in healthcare, the first thing that comes to mind is usually accuracy. We want the model to predict correctly, whether it’s diagnosing eye diseases, classifying scans, or detecting early signs of Alzheimer’s.

But here’s the truth I learned in my project is accuracy alone is not enough.

The Challenge I Faced

In my Alzheimer’s early detection project, the model was performing well on paper.

Laporan Klasifikasi (Classification Report):

                      precision    recall  f1-score   support

     Mild Impairment       0.97      0.99      0.98       179
 Moderate Impairment       1.00      0.92      0.96        12
       No Impairment       0.99      1.00      0.99       640
Very Mild Impairment       0.99      0.98      0.98       448

            accuracy                           0.99      1279
           macro avg       0.99      0.97      0.98      1279
        weighted avg       0.99      0.99      0.99      1279

------------------------------------------------------
Matthew's Correlation Coefficient (MCC): 0.9781
------------------------------------------------------

The numbers looked impressive, but there was still one big question:

How can we trust what the AI sees?

Doctors won’t just accept a probability score. Patients and their families won’t feel reassured just by a number. They need to know why the model made that decision.

Discovering Explainability with Grad-CAM

That’s where I explored Grad-CAM (Gradient-weighted Class Activation Mapping).

Don’t worry, it’s not as complicated as it sounds. Grad-CAM creates a heatmap that highlights the regions of an image the model focuses on when making a prediction.

In other words, it turns the “black box” into something more transparent and human-readable.

Before and After Grad-CAM

In my Alzheimer’s project, the difference was clear:

Before

The model predicted “Mild Demented” with high confidence, but I had no way to explain why.

After Grad-CAM

The heatmap showed exactly which parts of the brain scan the AI considered most important. And more importantly they were the medically relevant regions linked to early Alzheimer’s symptoms.

That small shift made a big difference. Suddenly, the model wasn’t just a silent judge giving out labels. It became a tool that doctors could actually discuss, question, and trust.

What I Learned

This project taught me a valuable lesson:

Accuracy is powerful, but explainability is what builds trust.
In sensitive areas like healthcare, trust matters as much as performance.
Tools like Grad-CAM are not just technical tricks, they are bridges between AI researchers and medical professionals.

Final Thoughts

Working on this project reminded me why I got into AI research in the first place: not just to build models, but to build models that people can trust and use.

Explainable AI is not optional anymore. It’s the key to making AI truly impactful in real life especially in areas that touch human health.

See the full notebook of Alzheimer Detection here: https://www.kaggle.com/code/hafizabdiel/alzheimer-classification-with-swin-efficient-net

If you’re curious about my other AI projects, you can find them here: http://abdielz.tech/