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.