1. What are 2–3 things that surprised you or shocked you in the film/video/podcast?

One thing that really surprised me in both the podcast and video was learning how much AI is driven by marketing and hype rather than actual “intelligence.” Timnit Gebru explained that companies use the term AI very broadly to make products sound more advanced and futuristic. Reflecting on myself, I realized this strategy actually works very effectively because whenever AI is mentioned, I automatically view a product as more impressive or innovative. Both the podcast and video showed how companies create excitement and fear around AI so people feel pressured to constantly keep up or risk falling behind. This made me realize how strongly media narratives shape the way society views technology. And, as much as I hate to admit it, I can also see how this will likely be useful in my future career in Integrated Marketing and Communications, since understanding the public’s mental shortcuts and associations is key to shaping effective campaigns and advertisements.

Another thing that shocked me was the amount of hidden human labor behind AI systems. Before this, I assumed AI worked mostly on its own, but the podcast explained how workers, often in poorer countries like Kenya, spend hours labeling data and filtering disturbing content for very low pay so these systems can function properly. The video reinforced this by showing how companies present AI as fully automated while hiding the real people behind it. I found it disturbing that something presented as “smart” and effortless actually relies on invisible and often underpaid labor that is rarely acknowledged. This built on what we discussed in class about the labour behind AI, but hearing it in more depth made the issue feel even more unsettling on a personal level. While I understand that companies aim to reduce costs, I also feel there should be stronger ethical guidelines to protect workers and ensure safer working conditions.

Lastly, I was surprised by how clearly both the podcast and video show that AI can reproduce discrimination and bias instead of eliminating it. Timnit Gebru explained how facial recognition systems and AI image generators often perform worse for women and people with darker skin because they are trained on biased datasets. This connected strongly to examples we discussed in class, such as AI generating nurses as women and doctors as men. Both sources reinforced the idea that technology is not neutral and often reflects the values and biases of the people who create it.

2. What was an important connection between what you learned in the film/video/podcast and things we did in the course?

An important connection between the podcast, video, and the course was how strongly media narratives shape the way people understand and respond to AI. Both sources showed how AI is often framed in news, advertising, and social media as revolutionary, unavoidable, and necessary for the future. This connects to what we discussed in class and how globally, there needs to be an awareness/ AI literacy of the bias within AI, or there will be a high chance of westernization and belief in a single story/narrative. In my Soliya discussions, different members discussed how their countries viewed AI. Although Italy views AI as a tool to progress, countries like Tunis saw it as the missing piece to fix their education system as it will help accessibility amongst the masses thus resulting in a higher education level amongst the population. But the fear is, if countries are blindly following the biased content produced by AI, the thoughts and culture of their nation will transgress away from them as they adopt westernized education content as that is what they are getting exposed to, thus there is a higher chance of losing their identity.  

It also connected to our discussions about digital inequality and ethics. Timnit Gebru explained that AI does not affect everyone equally, since large corporations gain huge profits they exploit workers through underpayment, suppressing marginalized communities narratives to suit their interests and overall overconsumption of data collection of all users globally. The video reinforced this idea by showing how systems that appear neutral actually depend on unequal global structures. Together, both sources strengthened my understanding that AI is not just a technological issue, but also a social and economic one shaped by power, inequality, and corporate interests.

3. Given who you are as a person, your identity, and your life experiences so far, what is something you will do differently based on what you learned in the video?

After watching the video and listening to the podcast, I think I will become more intentional and critical in how I use AI and digital tools in my everyday life. I cringe as I write this, but the other week, I needed to fill out a form filled with personal data, like my full name, date of birth, national ID, etc, in order to compete. As this all had to be done in Arabic, I put it all in AI and asked it to translate. Now having engaged with the content of the podcast and video, I’m aware of how stupid that action was, as they now have all my personal information. As a student, I already rely on these tools for convenience, but I realize I often do not question the severity of the data I input.  The idea that AI systems are built on hidden labor, biased data, and corporate interests makes me want to slow down and think more carefully before  using AI as not only am I harming myself through a self induced  privacy breach, but by harming myself I’m allowing others to undergo harsh labour conditions. The only person winning is the villain. And I’m aware that villainizing AI is very dystopian, but it’s always the ones you trust the most that stab you in the back. 

4. What is something you think should be covered in the course in future, based on what you learned in the film/video/podcast, which was not already covered in class?

I  think the course should go deeper into the hidden human labor behind AI, since the podcast showed how much AI depends on underpaid workers doing tasks like data labeling and content moderation, which is often completely invisible to users. I think in general, bringing more awareness to the outer negativities of AI like the environment too is also important and often something nobody is aware of, or at least I definitely wasn’t.