During todays class, I used ChatGPT as a generative AI source, while other members of my group used NotebookLM. We made an attempt to research and discover AI hiring tools as a topic and see which sources came up to compare reliability. Group members that did not use ChatGPT found sources like “15 best AI hiring tools” and “10 Best AI hiring sources.” While using ChatGPT, I found sites that were about AI sourcing and outreach such as hireEZ and Zoho Recruit. I believe that ChatGPT is more reliable because I have used it to find me sources on topics, and I’ve used it for citations to format properly. Often, it’s easier to do because you can just copy and paste the source into the search bar and then ask it to format the source properly. I’m sure others prefer to use NotebookLM, but I’ve seen both sides and I have used what I prefer. I do not have any plans to use Ai any differently going forward, but it was brought to my attention that ChatGPT is not great for sources to find, and I have to be more careful with the usage of it while avoiding bias. This is something I’m taking into consideration moving forward heavily. If I’m being honest, I don’t think AI is headed in the right direction, I believe it’s taking over for the worse and not the better. It is taking up things like job opportunities, energy, and data. This can be a massive problem in the future. An argument from another groups findings was about the trajectory of AI and how people also think it’s getting worse. It’s scary to look at the many ways robots can project information as well as personal data.

Sources ChatGPT pulled up:

Resumly. “What AI Tools Recruiters Use for Screening: 2025 Guide.” Resumly AI, https://www.resumly.ai/blog/what-ai-tools-recruiters-use-for-screening-2025-guide. Accessed 12 Mar. 2026.

HireGen. “Top 5 AI Recruitment Tools You Should Be Using in 2025.” HireGen, https://hiregen.com/posts/top-5-ai-recruitment-tools-you-should-be-using-in-2025. Accessed 12 Mar. 2026.

Scalar. “The 10 Best AI Recruiting Tools to Supercharge Hiring in 2025.” Scalar, University of Southern California, https://scalar.usc.edu/works/the-10-bestai-recruiting-tools-to-supercharge-hiring-in2025/index. Accessed 12 Mar. 2026.

ChatGPT is strange unless you’re direct. The most strange thing about ChatGPT’s model was that my prompt had to be really specific in order to find academic sources. First I asked it for academic sources related to facial recognition involving policing, environmental studies, or healthcare. It came up with journal articles and one YouTube video, but nothing from a book or database. After my third ask, I was able to filter down 5 from a database and then put those into Notebook LM. Notebook summarized these sources together and claimed that “Facial recognition technology (FRT) in policing creates a conflict between surveillance efficiency and democratic accountability. Public support is often performative; anonymity reveals many citizens privately harbor reservations about biometric tracking. Empirical data shows FRT deployment correlates with increased racial disparities, specifically raising Black arrest rates while decreasing White rates. This stems from automation bias and pre-existing structural inequities. Global regulations remain fragmented; the US lacks the robust accountability frameworks found in the EU, necessitating urgent, transparent impact assessments to protect civil liberties”. Through this lab, we learned that both AI models have strengths, but Chat is not going to excel at what Notebook excels at and vice versa. Chat struggled at finding these sources and struggled even further on giving me great summaries of the sources they provided as it was multiple steps. My prediction about where AI is headed is a continued reliance because it comes up with sources instantly rather than a trip to the library or a database. I learned that Chat says its prompts very confidently and if you do not check it for error, you are using it incorrectly.

One of the most surprising things I learned during today’s research lab was how widely AI hiring tools are already used and how much influence they can have on the labor market. Many companies now rely on AI systems to screen resumes, rank candidates, and even evaluate video interviews before a human recruiter ever reviews an application. At first glance, this seems efficient. These tools can process thousands of applications quickly and reduce the time and cost of hiring. However, what surprised me the most is that these systems can unintentionally reproduce existing social biases.

Because many AI hiring tools are trained on historical hiring data, they often learn patterns from past decisions that were already biased. For example, an experimental hiring algorithm developed by Amazon reportedly penalized resumes that included the word “women’s,” because the system had been trained on data from a male-dominated tech workforce. This shows how algorithms can replicate discrimination even when companies are trying to automate hiring objectively.

The consequences go beyond individual applicants. If biased algorithms systematically filter out qualified candidates from certain groups, this can reduce equal access to employment and reinforce existing inequalities in the labor market. Over time, that can also affect the broader economy. When companies fail to hire the most qualified workers because of biased screening systems, the result can be poorer job matching, lower productivity, and wider wage inequality. Researchers from the Brookings Institution argue that biased algorithmic hiring can limit opportunities for marginalized workers and reduce the overall efficiency of the labor market.

Overall, this research made me realize that while AI hiring tools promise efficiency, they also raise serious ethical and economic questions about fairness, opportunity, and the future of work.

Hi everyone! My name is Othmane (he/him). I’m a senior from Morocco. I’m really passionate about music and DJing, and when the weather is nice I like to play soccer or go swimming. I’m excited to take this class because AI seems to be advancing much faster than the law, and studying the ethics around it feels especially important right now. Personally, I’m curious to see how AI develops. Ideally, I’d love for AI to take care of things like laundry and dishes so I have more time for art and music, not the other way around.

My name is Jordy I am a Sophmore majoring in Computer Science and I come from Rwanda. In my free time I like to play basketball, golf, listen to music and video games. My favorite player of all time is the goat himself Lebron James. One thing I find interesting about AI today is how quietly we use it without even realizing it. For example, apps like Spotify use AI to recommend songs and create playlists based on what we listen to. Most of the time we don’t think about it, but AI is constantly working in the background to shape our experience and help us discover new music.

Hi everyone! My name is Adeline (she/her/hers) and I am a Senior from Ghana. Some of my hobbies include trying new recipes and writing. I am excited to take this class and as far as my thoughts on AI’s role in our lives, I think that AI is embedded in far more than we know or recognize and there are both upsides and downsides of that. AI can be a super useful tool to streamline several work processes and save time, but there is surely a cost(s).