During class, me and my group researched AI hiring and the tools it uses. Here we found a lot of shocking and new information that I was not aware of. The biggest one being between a male and a female applicant, like yesterday the AI device is more bias toward the male body and the male application.

Now it does go through the whole process with both but overall, in the end it is more bias toward the male body than the female. I also found that AI only puts people in there job-fitting 40-67% of the time. I think this is a bad thing for AI to be doing because give or take 5-10% of the time people are not going to be doing what they applied for/what there job fit actually is. I do think though in the near future more and more companies will pick up of AI hiring and it will become more accurate and better through the years. I think that it will develop more but it will only come with time and trial.

Something I did learn and that is useful is that through the hiring process, it saves humans a ton pf time with going through hundreds and thousands of applications where humans can only do so much at a time. Also through the resume screening, AI has a 92-94% accuracy rate or picking the best candidates for the specific hiring.

ChatGPT information verified by-https://www.theguardian.com/technology/2018/oct/10/amazon-hiring-ai-gender-bias-recruiting-engine?utm_source=chatgpt.com

https://arxiv.org/abs/2407.20371?utm_source=chatgpt.com  

For the most part, people tend to think of artificial intelligence as something futuristic, like robots, ChatGPT, or self-driving cars. But the strange thing that I found out during our research lab is that most people are already utilizing artificial intelligence every single day without even realizing it. It is just running in the background of the apps that we use on a daily basis.  

 One of the things that really caught my attention is the music streaming services that we all use, like Spotify. The playlists that we get from Spotify, like Discover Weekly or Daily Mix, are not random. They are generated based on our listening habits compared to millions of users across the platform. In a way, artificial intelligence is dictating the type of music that we will probably listen to without even realizing it. 

The ethical issue that really stood out to me is the fact that this influence can be so invisible. When AI systems are making decisions for us, for instance, the type of music or news that we are exposed to, they can, without our knowledge, influence our preferences, as well as our worldview. The biggest surprise for me during my research was the fact that the influence of AI can be so invisible. Sometimes, AI does not necessarily feel like “technology making decisions.” Sometimes, it feels as though the app somehow knows you really well.  

 Another thing that I learned from the research lab is the fact that the way you talk to the AI can really matter. One of the very helpful techniques for interacting with large language models is the fact that you can give them context. Instead of asking vague questions, giving them specific prompts can really work for you. 

AI is becoming part of everyday life often quietly in the background. The more we understand how it works, the better we can decide how much influence we want it to have. 

Source 

MIT Technology Review. “How Recommendation Algorithms Work.” 

Spotify Engineering. “How Discover Weekly Works.” 

In the research lab, my group decided to research the ethical issues related to AI facial recognition technology. In my research, I found that there are two major problems with the use of AI facial recognition technology. The problems are:

First, the facial recognition technology is not equally efficient for all people. Research indicates that the technology is more efficient for white male faces than for women or darker-skinned individuals. The National Institute of Standards and Technology conducted a study that indicated some programs have higher false positive rates for certain racial and ethnic groups. This means the system is more likely to incorrectly identify a person with another person’s face in the database.

Another problem with the use of facial recognition technology is the problem of misidentification, which can lead to the wrong person being arrested. There are already reports of the wrong person being arrested because the facial recognition system incorrectly identified the person. The American Civil Liberties Union (ACLU) reported many cases of the wrong person being arrested because the system incorrectly identified them.

The most surprising thing I learned was how widely this technology is already being used even though researchers have found major accuracy and fairness problems. This made me realize that AI systems like facial recognition can have serious real-world consequences when they are used in law enforcement and security systems.

1. https://www.nist.gov/programs-projects/face-recognition-vendor-test-frvt (NIST)
2. https://www.aclu.org/issues/privacy-technology/surveillance-technologies/face-recognition-technology

In today’s lab our group decided to research on Ai as a hiring tool, I’ve never really heard about it before coming to this class, I’m not sure if it’s because from coming from a different culture where it’s not being as used or it’s just not talked about enough. This was honestly really surprising and scary at the same time, especially after professor Hayward told us about “Kevin situation”

Since many Ai tools are trained on historical hiring data, they’ve learned patterns. In this case Kevin was the most common name in this company so the Ai tool automatically put Kevin as the best candidate for the job. This makes me wonder if Ai keeps growing and is being used in important situations as this while promising efficiency, it could cause more harm and unfairness to people than good.

In this lab we also split into 2 different groups, one working with ChatGPT and the other with notebook, which made me notice some differences. ChatGPT was answering more in summarized short answers and in bullet points while NotebookLM was giving longer responses. ChatGPT also kinda kept repeating the same source page just posted from different people while NotebookLM was giving more diverse responses. So this lead me to a conclusion that ChatGPT is more efficient in making short notes with bullet points, lists, workouts, etc.., but NotebookLM seems more useful for actual learning and curiosity.

https://arxiv.org/abs/1906.09208 – website that kept repeating

https://jier.org/index.php/journal/article/view/3262

Before I conducted any research, I thought the biggest ethical issues around AI were things like bias or misinformation. I had very little idea of the massive water crisis spawned from the AI boom.

In the MIT’s article’s breakdown of generative AI’s environmental impact, A single ChatGPT query consumes about five times more electricity than if you searched the same thing on Google (Zewe). That’s not even the biggest piece however, the actual training of the AI systems consumes an absurd amount of resources. The article states that training a model like OpenAI’s GPT-3 consumed roughly 1,287 megawatt hours of electricity (Zewe). It’s important to note that these numbers are just the training phase and the energy demands keep piling up every time anyone uses the model. It’s not even just electricity, water use was already a big problem that has only been made worse by the AI industry. For every kilowatt hour of energy a data center consumes, it needs around two liters of water for cooling (Zewe) and these facilities are pulling from real municipal water supplies and affecting local ecosystems.

Going forward, I’m going to be more intentional about when and how I use AI tools. Not every question needs a ChatGPT prompt sometimes a search engine or my own brain is perfectly fine.

Works Cited

Zewe, Adam. “Explained: Generative AI’s Environmental Impact.” MIT News, Massachusetts Institute of Technology, 17 Jan. 2025, https://news.mit.edu/2025/explained-generative-ai-environmental-impact-0117

We’ve all heard of ChatGPT but not Notebook LM… It honestly took a while for me to learn how NotebookLM works before I could experiment with it in today’s lab. But after playing around with it for a while, I could see its usefulness and how it can be implemented into my research procedures going forward.

My research topic was Racial Bias in Face Recognition Algorithms. I first went into ChatGPT and asked it to provide multiple sources discussing this topic. I clicked on the first three and they were all pretty good. I had free access to all of them, I could download them easily, and they were all relevant to the topic. Then, I asked both ChatGPT and NotebookLM “what issues do all three sources raise regarding this topic?” Both platforms provided 6-7 bullet points with most points overlapping. This was a surprise to me. I initially expected NotebookLM to do better on it as it allows users to specifically upload documents and discuss about it whilst ChatGPT allows you to do pretty much anything. However, I would still not trust ChatGPT to be 100% accurate since it may simply be stating whatever is most common out there. With this in mind, I would likely use ChatGPT to find sources given that I can click on their links and verify their accuracy and legitimacy myself. I would also use it for simple, quick searches that do not require analyzing sources or research papers and can instead be answered using general information available on the internet. However, if I am trying to dive deeper into each source or combine and compare multiple sources I have found, I would use NotebookLM. I think it is important to keep in mind the purpose or goal of what I am trying to achieve as well as to understand the strengths and weaknesses of each AI tools when deciding which one to use.

https://pubmed.ncbi.nlm.nih.gov/33585821/?utm_source=chatgpt.com

https://www.mdpi.com/2079-9292/13/12/2317?utm_source=chatgpt.com

https://link.springer.com/article/10.1007/s43681-021-00108-6?utm_source=chatgpt.com