(Thought I posted this at 9:47am, just saved the draft better late than never)

As with anything that involves using information, Artificial Intelligence , and releasing that online for the world to see there are ethical concerns that arise when thinking about how you’ll execute a project. My project focuses on International Film Distribution. Still in its early stages I plan to use or create a dataset of perhaps 30 or so films from both inside and outside the U.S. that debuted in Theaters . Using variables such as Title, Year, Country of Production, Distributor, Domestic release date, International release date, Genre, Director, Language, Box Office. This not only to create an understanding of Film distribution but also to create a sense of how these markets work with each other and how censorship, politics and other factors influence what gets released and where. So part Data Visualization part Digital Storytelling.

Data Collection: This one is interesting. Much of the data I would collect would be public, However if I only use easily accessible databases, my dataset could unintentionally favor films from certain countries, industries, or time periods. I probably need to clearly say where my data comes from, how films were selected, and what limitations or gaps exist in the dataset.

Privacy: Privacy would probably be a relatively minor concern because the project would primarily examine films, distribution companies, countries, and publicly available information. Avoiding personal details about directors and those involved in the filmmaking and distribution process should relatively simple.

Transparency and Providing Context: Visualizations can make data appear more objective or complete than it actually is. I will clarify what criteria I used to include films and what may be skewed in the visualization. For example, a map showing that one country distributes more films internationally than another does not necessarily mean that its cinema is more culturally influential and so on.

AI Use: Environmental Concerns: If, and that’s a big IF, I use AI tools during the project like to brainstorm, organize information, analyze text, or assist with coding and such there is a potential environmental concern because AI systems require computing resources and energy. Noman Bashir of MIT brings a great point when saying “What is different about generative AI is the power density it requires. Fundamentally, it is just computing, but a generative AI training cluster might consume seven or eight times more energy than a typical computing workload,” Using an LLM model to even help brainstorm a dataset outline would use ridiculous amounts of energy. In the end it would be better to just avoid AI use as much as possible.

Accessibility / Universal Design: Accessibility should be considered when creating the project’s designs. A visualization that depends entirely on color, for example, may be difficult for some users to interpret, while an interactive map may be difficult to navigate with a keyboard or screen reader. I should provide readable labels, good contrast, descriptive captions.

Generative AI usage: No Gen was used in the writing of this module

Sources: https://news.mit.edu/2025/explained-generative-ai-environmental-impact-0117?utm_source=chatgpt.com

https://www.iubenda.com/en/help/181591-inclusive-design/

The Brainstorming session on Wednesday 23rd, 2026 has left me with two possible topics for the Final Project. All which equally pull my interest. However some might be easier than others. The first topic is inspired by our discussion on the use of LLMs in academics. This time it would be looking at the use of LLMs in the scientific or political fields. This idea stemmed from the recent reports that U.S. military nearly launched an attack on a Chinese Vessel when an AI chatbot an analyst had used inaccurately identified the material the ship was carrying as nuclear weapons. Despite this being the first topic I came up with, this is the less developed of the two in terms of how I would execute it as a Digital Humanities project. However I did think of a few questions I could ask: What are the potential risk of LLM mistakes when used militarily? Do AI-generated campaign messages become more standardized than human-written ones?

The second idea is the one that ended up exploring during class, that being International Film Distribution. The will be a combination of data analysis with a sprinkle of digital storytelling. The questions I had were as follows: What does the network of international film distribution look like, and how has that network changed over time? Does international film distribution create a genuinely global network, or does it concentrate around a relatively small number of cultural and economic hubs? How did films move between countries, and what geographic patterns or inequalities emerge from those distribution networks?

The next part was figuring what skills I wanted to improve and how that would influence they way I would end up executing this project. Not entirely satisfied with the visualization of the Dog Breeds data, that would be a skill I would like to improve. It terms of tools I had no clue. I did participate in the LLM prompting in class, and I’m not gonna lie it didn’t help as much as maybe I had hoped. However most likely I will either build or find a data set that fits within my questions and then maybe then visualize the data as a network while also taking into account how disproportionate some of the data might depending on how much data we have on film distribution in certain areas. (It’s a little late uploading, Voices really didn’t want me to upload an image for some reason)

AI use: No artificial intelligence was used in the writing or editing of this blog post.

The most interesting thing I learned about LLMs was how GenAI can not only produce bias information but also that overall A.I. bias can be more extreme than real-world bias. This was covered in Jon Cheung’s article for The London Interdisciplinary school. I think most people don’t really think about what a generative A.I. model is really doing when it’s asked to make an image. What makes it worse it the fact that the biased images the generative A.I. produces that are uploaded to the internet will then by fed back to it. For example, if you ask an image generator for a series of pictures of CEOs there is high chance that it will mostly produce a mid 40s white make in suit standing and smiling in a boardroom. If you upload that to the internet that those images can then be used to train the same or another A.I. image generator.

Another interesting thing was learning how exactly the token system worked. I didn’t know that A token is not always a full word. It definitely can be a whole word but it can also be a fragment of a word, a phrase, or individual letters. Then the text generation happens using the probability distribution across every token in its vocabulary for what should come next. Real good semi-complicated stuff.

In terms of my uses for A.I. I only plan to use it for organizing/ summarizing given information. I don’t use it for anything outside of that.

Citations/Sources

Cheung, J. (2023, August 21). How AI image generators make bias worse. London Interdisciplinary School. https://www.lis.ac.uk/stories/how-ai-image-generators-make-bias-worse

3Blue1Brown. (2024). Transformers, the tech behind LLMs | Deep Learning Chapter 5, YouTube. https://www.youtube.com/watch?v=wjZofJX0v4M

Declaration of AI Use: No generative A.I. was used in the writing of the blog post, nor for grammar.

One of the questions my group came up with was, how is space used on The Voice? For example perhaps the football game last Sunday is given a page and half worth of pages while a student protest is given just 2 columns. Another question was what type of content was included? We also asked who is the target audience? Did the target audience ever change as time went on? These questions are all interesting because a few show the priorities the editors for the voice had when it came to what information was shared. It also gives insight to just how many resources the editors and writers had just to be able to write about and share the information they gathered. (wasn’t able to upload image……kept talking about sever issues so enjoy the pretty colors) Photo site: Editors, Wooster Voice, “The Wooster Voice (Wooster, OH), 1969-02-28” (1969). The Voice: 1961-1970. 191.
https://openworks.wooster.edu/voice1961-1970/191

In terms of organizing a spreadsheet for the first question the goal would be to compare how much physical space(pages, columns, etc.) different topics receive. The rows would then be each article/item published in The Voice and columns would include; the title, date, category ( sports, campus events, politics), and an approximation of the exact amount of space used. For the question of type of content it would be more of less the same perhaps adding sub-categories or tone to help find keywords. For target audience rows would be, again, articles and columns would be things like; department, subject, tone

In terms of data privacy including student names would of course make if personally identifiable information. Articles about protest and personal stories and those involved most likely didn’t consent to being part of reaserch dataset. A way this could be handled carefully would be to anonymize authors, avoid storing personal details, and focus on content categories.