1. Things that surprised me or something new I learned

After listening to Timnit Gebru’s podcast, Don’t Fall for the AI Hype , and watching Model Employee, I kinda got stuck on one thing that surprised me. It was like how much AI depends on hidden human labor. Before all this, I mostly pictured AI as something made by engineers and computers, plus data, like the whole thing just happened in a lab. But the podcast sort of made it click that a lot of AI systems also rely on people who label, sort, filter and clean data . For instance, TIME reported that Kenyan workers were paid less than $2 an hour to tag disturbing , and harmful content so ChatGPT could become less toxic. That really shocked me, because the final product looks polished and advanced, but behind that polished surface there may be humans doing emotionally difficult work, work that users never see.

Another takeaway I got from Gebru is that the AI hype can trick people into thinking these systems are more intelligent, or more neutral, than they actually are. Instead of treating AI like magic, she kind of nudges us to ask who built it , who benefits from it, and who ends up harmed by it. Model Employee also surprised me in a different way. It showed a tracking wristband being used on a restaurant worker, Neeta, and the tech followed her life beyond her shift. That felt pretty out- of-the-box, because it wasn’t just “robots replacing workers” as a simple story. It was more like technology slowly sliding into someone’s body, time, privacy, and freedom piece by piece.

2. Connection to what we learned in the course

An important connection to the course is this idea that technology is not neutral, like it never really sits there quietly. In class we talked about how digital systems get shaped by society, politics, business interests and inequality. The podcast connected too because Gebru is saying, basically, that the way people talk about AI often covers up the actual social stuff that is going on, like it hides it behind nice words. For instance, if a company says an AI tool is “efficient”, that sounds cheerful, but it might also mean workers are being observed more closely, assessed by an algorithm, or pushed to work faster. Model Employee connected strongly, for me at least, because Neeta’s wristband wasn’t just a workplace gadget. It turned into a kind of symbol, of surveillance and control, very much so. That reminded me of class discussions on how technology can keep reproducing power imbalances, you know the kind where the employer has more leverage. The employer has more information and more control, while the worker ends up with less privacy and fewer options. An out-of-the-box connection I thought of is school surveillance technology, like exam proctoring software. It gets marketed as something that prevents cheating, but at the same time it can make students feel constantly watched and graded by systems that might not understand normal behaviour, or they misread it. In a similar way, AI at work can be framed as “helpful,” even while it is creating stress, fear, and uneven treatment.

3. What I will do differently based on what I learned

Based on who I am really, my identity, and all the little life things I been through, one thing I will do differently is become more intentional about how I use and talk about AI. I use technology for my education, for chatting with people, and just for everyday tasks, so it is kind of natural to only focus on convenience. But after the podcast and Model Employee, I don’t want to treat AI as automatically impressive just because it is fast, or because it is popular. I want to pause a bit and ask better questions, like Who made this system? What data trained it? Are people being monitored, or is it less obvious? Is someone’s labour being hidden, under the hood? Could this tool make life harder for workers, students, immigrants, low-income people, or people from marginalized communities?

Like, for example, if I use an AI tool to help within my education, I can still check the information myself instead of just taking it. I can cross reference, even if it takes longer. And if I hear a company saying they want to use AI to “improve productivity,” I’m going to stop and ask what that even means. Does it mean supporting workers, making things safer and easier, or does it just mean squeezing more labour out of them?

Also, personally, I want to be more careful about sharing private information with AI tools. Model Employee really stuck with me, because data collection can go farther than most people expect. So, my action plan is basically to use AI more critically, not just more conveniently.

4. What should be covered in the course in the future

Honestly, based on what I learned, I feel like the course really should cover more of the human labour behind AI and the workplace effects too. We hear “AI” like it’s only about coding, algorithms, or some kind of future invention, but I think students should get into the people part as well. Like data labellers, content moderators, delivery workers, warehouse workers, restaurant workers, and office workers whose jobs get pushed around by algorithmic management.

Maybe there could be a unit called “the people inside the machine” or something similar, where it’s clear that a lot of AI systems run on low paid or basically invisible labour, not just shiny tech. And then, yeah, workplace surveillance should be treated more seriously. Not only the simple stuff, but technologies that track keystrokes, location, productivity, emotions, or even customer interactions, like the systems that watch how people behave and then adjust things.

Another topic that feels useful is AI transparency, for example “model cards.” Those are documents that explain what a machine learning model is meant to do, how it was tested, and where it can fail. That could help students go past only criticizing AI and instead start thinking about what responsible AI should look like in practice.

Overall, I think future classes should spend more time on the bigger question, not just “What can AI do?” but more like “What kind of society does this AI create?”