AI 101

What you need to know

What we’ll cover:

What AI is
Examples of AI in your life
How AI works (conceptually)
Why AI is (seemingly) everywhere
Responsibly using AI
Examples of how to put this information into action

Goals for the Training

Understand at a basic level what AI is
Understand that there are different kinds of AI models
Understand different AI models are appropriate for different tasks
Be able to apply this information to evaluate if an AI model is probably appropriate
Understand why the term AI has become seemingly ubiquitous
Have a basis for further learning

How would you define AI?

“AI is whatever hasn’t been done yet.” — Tesler’s Theorem¹

Defining AI

How do you define “Intelligence”?
Academic definitions differ and are not always helpful for practical use

AI Definition Overview²
	Human-based	Ideal Rationality
Reasoning-based	Systems that think like humans	Systems that think rationally
Behavior-based	Systems that act like humans	Systems that act rationally

Defining AI

AI is “an engineered or machine-based system that can, for a given set of objectives, generate outputs such as predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy”³

Defining AI

Questions to help determine if it’s AI:

Does the technology use data to provide predictions, recommendations, insights, or decisions?
Does the technology augment or replace human decision-making?
Does the company use words such as “personalized”, “tailored”, and “adaptive” in its marketing?”
Would it be difficult or impossible to write out how a program does what it does, either because the process is opaque or because it is mathematically complex? ⁴

Examples of AI

What are some examples of AI you interact with on a regular basis?

Optical Character Recognition

Video Games⁵

Spam Filters⁶

How AI Works Conceptually

Models

Functional Paradigms

Predictive/Analytical AI
Generative AI
Agentic AI

Lets say you need an AI to help organize some recipes:

Logicist AI

Probabilistic AI

Neurocomputational AI

Think of logicist artificial intelligence as a series of rules. If you know all of your cake recipes are made with flour and butter and are cooked at 350 degrees, and none of your non cake are you can give that rule to an AI who can sort them based on the logic you gave it.

Think of probabilistic AI as showing a model a bunch of recipes labeled ‘cake’ and ‘not cake’ with notes on each ingredient; once it is trained you can show it a recipe and it’ll say how likely it is a recipe for cake or not

Think of “neurocomputational” as having a bunch of robots working in groups. You can show them a bunch of recipes, and each group will work to figure out one part of what makes a recipe. You can then ask them to label something as cake or not, or even potentially ask them to give you a new cake recipe. They could be super accurate (maybe they figured out some cakes are made with almond flour), or could be really off (they could think a recipe for wet cement is cake), because they don’t have the context to know what cake is, and aren’t good at telling you what they do / look at.

AI Model Type Overview
	Logicist	Probabilistic	Neurocomputational
good for:	clearly defined logic	clear factors	No clear process
bad at:	handling uncertainty	operating with limited /bad data or assumptions	Truth; Explainability
example:	email inbox rules	(some) population forecasting	chatGPT

To give another example of each: In my personal life, I’ve signed up for a few store rewards cards. I like looking at the flyers before I go, but I don’t want them cluttering up my inbox, so I set inbox rules to move those to a folder. Some of the companies use different emails, so I can’t create rules that always filter correctly because there is some uncertainty about what emails should / should not be moved.

At my last job, I had to predict how many cars we’d sell of certain types so we could buy appropriate stock. There aren’t clear, human definable rules for predicting that, but we had previous sales data. The data sucked, so the forecast had an upper limit on how good it could be. As a company we artificially inflated demand in December by running sales then, so we had to account for that outside of the Machine Learning model because the data was not accurate.

When I was a lab manager, one of the projects I worked on was creating an algorithm to sort hand-drawn paperwork. There were no clearly defined features I could use, and no clear rules about which was which I could communicate to a computer. I cross-trained a computer vision neural network to sort the documents into different folders but it couldn’t have told me e.g. why it sorted them a certain way.

Real Life Examples of Each Kind of AI

Logicist

Probabilistic

Neurocomputational

Optical Character Recognition

Video Games⁷

Spam Filters⁸

Lots of video games use AI to control NPCs (non-playable characters). For example, in Skyrim NPCs have to know where to walk without someone controlling them, how do they do that? How do NPCs know how much they should charge for a book, or spend on a sword you want to sell them? Newer video games often use a combination of several kinds of AI, so perhaps in Skyrim there would be a logicist AI to determine where NPCs can walk, and then a neurocomputational AI to determine where they actually walk.

Unfortunately, spam emails don’t usually tell you they’re spam, so how can spam filters work? It would take hours and hours of manual work to create rules that would work for the spam I get on my work email and my personal email, much less that would be mostly accurate for most people. This is somewhere ML excels- select an appropriate model, give it labelled data, and it can create rules to catch most spam.

Optical Character Recognition is used to e.g. add words to scanned PDFs. It’s usually based on Neural nets, which are especially good at computer vision

Functional Paradigms

Predictive and Analytical AI
- Recognizes patterns and makes predictions⁹
Generative AI
- Generates new content¹⁰
Agentic AI
- Achieves goals by executing multi-step workflows¹¹

Why AI Is Everywhere

What do you make of the interest in AI right now? Do you think it’s similar to anything else?

Context

This is not a comprehensive timeline and focuses on giving context for the current state of AI as relevant to this training.

TurboChamp¹²

The Stochastic Neural Analog Reinforcement Calculator (SNARC)¹³

Dartmouth Summer Research Project on Artificial Intelligence¹⁴

Eliza¹⁵

AI Winter¹⁶

AI Winter¹⁷

Learning representations by back-propagating errors¹⁸

Probabilistic Reasoning in Intelligent Systems

A Statistical Approach to Translation¹⁹

Software for the World Wide Web becomes Free²⁰

Rankdex Search Engine uses AI to prioritize sites²¹ Google’s page-ranking algorithm was influenced by Rankdex²²

Large-scale Deep Unsupervised Learning using Graphics Processors²³

Apple launches Siri²⁴

Attention Is All You Need²⁵

ChatGPT launches public preview²⁶

Linux Kernel adds AI agent documentation²⁷

AI agents become a focus for AI companies²⁸

First neural net model created on an analog computer using vacuum tubes
Workshop titled “Dartmouth Summer Research Project on Artificial Intelligence” credited as coining the term “Artificial Intelligence”
Eliza is the first chatbot and some believe they are talking to a human
This new way of learning lays the foundation for deep learning models
Introduced a new way of handling uncertainty using Bayesian networks which allowed AI systems to handle uncertainty
Marks a shift from rules-based to probabilistic methods in machine translation
This led to both an eventual explosion in data consumable by AI and needs best filled by AI (e.g. YouTube Recommendations)
Paper demonstrating that using GPUs can drastically speed up training models compared to CPUs, paving the way for more complex models
Siri represents a leap forward in the usability and accessibility of AI assistants
Paper by researchers at Google allowing neural nets to prioritize pieces of data in a way previously not possible
ChatGPT is launched as a free research preview. It uses a chatbot-like interface to interact with GPT-3.5 and marks a major turning point in public awareness of AI
Linux runs much of the technology you interact with on a daily basis, and has to be very secure to prevent wide-spread issues. There was controversy around Linux accepting AI-Generated code, and some issues with AI-generated reports wasting time. Their acceptance of AI generated code marks a major shift in broader attitudes towards AI-generated code.

Alan Turing created an AI which could play chess by designing an algorithm which would take the current state of a chess board, and then assign points to all possible next moves and choose the move with the highest points. Because of technical limitations, he never ran this on a computer.

Early neural nets had to be written by hand on punchcards- if you messed up somewhere in the middle you had to completely start over. There’s a hard upper limit on how much you can do in a system like that based on how many people you can have work on the project.

AI history is full of booms and busts- we figure out something cool and then over promise and under deliver. So people lose interest in AI because they think all of it is overhyped instead of just some, and then things that objectively are AI stop being called AI. This probably includes things you use every day, but don’t think of as AI.

GPUS are designed to run multiple processes at the same time, so models that would be too complex to run with CPUs because of the time became practical with GPUs

The increase in access to data is due to many factors, including the normalization of data collection e.g. by grocery stores and smart phones, but the easiest to point out is the rise of the internet. It’s a lot easier to scrape a few dozen websites and get usable data compared to trying to convert printed books into a format usable by ML. These changes have also created new (or amplified existing) use cases- like Netflix and Amazon wanting to show you things you might want to watch/buy.

Bayesian probability can handle uncertainty better, and is often more useful for ML than frequentist methods (come to AI 102 for a crash course), but requires a lot of computation which was impractical before modern computing.

A paper put out by computer scientists at Google in 2017 introduced a new way of adding context to things as they were processed by neural nets. Come to AI 102 if you want to learn more, it’s super cool, but the important part is that it created huge advances in the field

Image generators and neural nets have existed for years now- back when I was in college there was a Tumblr blog that was a bot someone trained on their own blog (inspired by a webcomic) that ran from 2019-2024. The bot was fun, and was basically a research project by the person who developed it that took many hours to set up, and many more to keep working long term. Until chatGPT, advanced ML models were not available to casual users except in end products. Most casual users had access to things like google translate, but probably didn’t know it was AI much less anything about how it worked. Honestly, most people still don’t know anything about how the AI in their lives work, but it is less intimidating now that you can go to a website and “chat” with a bot instead of having to learn basic coding and already know that machine learning exists. We’re in the middle of a boom of AI- lots of novel research is being done and right now the public is more aware that AI can do cool things, so it’s cool to call things AI. Some of this is hype by people who are excited about AI, or are taking advantage of those who are excited, and this hype (and again, the genuinely good research) means there’s more money going to AI right now.

Using AI

How do you use AI in your daily life, and what do you need to use those tools properly? Can you think of any non-generative AI tools you use?

Choosing the Right Tool

Right model for the job
- Probabilistic vs deterministic
Right Functional Category
- Generative vs Agentic vs predictive/analytical
Other things to consider
- Security and Privacy concerns
- Integration with existing tools
Is AI the best solution?

If I create a website for online shopping, and you judge it based on whether or not it lists states with the letter “R” in their name, that’s not really fair unless my website is the “states with the letter”R” in their name shop”

If you need specific wording or existing data, probabilistic AI is not going to be appropriate. If you want to organize pdfs, probabilistic AI is more likely to work (unless you have a good file naming scheme) because it can create rules to determine which files are similar to one another.

Even once you know what type of AI model you need, you need to determine the correct tool.

There are times where AI is not the best solution. If you want to check the accessibility of a website, there is a lot you can do with automation (e.g. color contrast), but some of it is easier to just check than try to create AI for (full screen-reader compatibility).

Let’s say you want to write Planning 101 guide for ETDD to give to local planners to help them make better choices (and maybe sell later), but you’re on a time crunch so you want to use AI to help you.

What model types and functional categories could you use and how?

Using the Tool Safely - General Considerations

The tool might work differently than you expect
Bias
Legal Grey Areas
- AI Notetakers²⁹
- State laws
- Mostly applying existing laws to AI
Remember that it is a tool

Using the Tool Safely - Generative and Agentic AI

Malware³⁰
Over reliance on the tool
Difficult to assess quality³¹
Accelerating how fast you can mess up
The tool doing something you didn’t tell it to do³²
Mental health concerns³³

Using the Tool Safely - Interacting With Others

Lost of trust with communities
Loss of quality
Privacy Rights³⁴
Public Opinion

Broader Concerns with Societal Use of AI

Software Supply Chain Issues
- Malware
- Negative Impacts to Open Source Software³⁵
Copyright³⁶
Loss of entry-level positions
Loss of Trust³⁷

Thinking about the Planning 101 guide and how you might use AI to help you, what are some concerns you might have with the different ways you could use AI? Are there any potential harms you might be able to mitigate?

Using the Tool Responsibly

You are the one using AI, even agentic AI
Automation Bias³⁸
Balancing Use
Make sure you understand and respect the limitations of tools
- Don’t use AI search results without verifying with a legitimate source
- Don’t give agentic AI access to anything you’re not willing to lose

The AI search is unreliable (the robots know what a fact looks like, not what it is), you need to verify any information with reliable sources (like in our policy)

Generative AI is subject to unique ways of hacking that traditional software is not, and cybersecurity experts are just starting to get a sense of the scope of the problem. One area of particular interest is with coding- if you use generative AI to code there are several ways it can pose cybersecurity risks that are elevated compared to coding by hand. Even if you don’t open CoPilot and ask it to code for you, if you use low or no code tools, most of those use generative AI. The legal realm around AI is currently rapidly changing and uncertain. A lot of where we’re currently seeing these changes is with figuring out how current laws apply to new(er) AI tools. For example, multiple AI Notetakers are in court trying to determine things like if collecting biometric information without proper consent is breaking California privacy law, or if sending an AI notetaker to a meeting you don’t attend might be breaking federal wire tap law. You could potentially use AI in a way that leads to a loss of trust with our communities, for example if you use generative AI to create incorrect statistics and use those in a grant and don’t get the grant as a result, our communities might not want you to apply for grants any more. Due to automation bias, it’s really easy to produce worse-quality material (e.g. a grant application) that you feel is better because it came from a more automated source.

In Data Science, a common refrain is garbage in garbage out. At an extreme, if a model was trained on 4chan, you’re not going to get professional emails out of it. For a more realistic example, if a transcript generating model was only trained on men with british accents, it won’t do a good job transcribing meetings we’re in. Data collection can also affect how the company uses your data- e.g. most AI note takers use your data to train their models, which makes them not appropriate for meetings where someone might say something you don’t want in a model you can’t control.

There are currently several court cases working to determine if training AI on copyrighted works without permission is considered fair use, and if there are instances where it might not be. Those cases could significantly impact many of the popular models today.

Right now, public opinion is still highly polarized on AI. Some people have serious ethical concerns with how a lot of the larger LLM models have been trained or are being used (e.g. to replace workers), while others consider any use of AI to be inherently unethical. Navigating this landscape will require us to be aware of opinions and considerate of them even if we disagree with them (both pro- and anti- AI).

Putting It In Action / Hypotheticals

Let’s say you want to predict the future population of Knoxville. A company is offering to use AI to forecast the population for the next ten years.

What kind of model(s) or functional categories would not be appropriate?

What could you ask them to evaluate if their methods work?

Would you feel confident in your answer, or would you want to ask someone who is more familiar with forecasting?

Thank you

References

2023. https://www.newyorkmyc.org/ai-generated-mushroom-books-life-and-death/.

2025. https://news.bloomberglaw.com/litigation/wiretap-suits-pit-old-privacy-laws-against-new-ai-technology.

2026. https://www.ibm.com/think/topics/history-of-artificial-intelligence.

n.d. https://commons.wikimedia.org/wiki/File:Email-spam-sample_(cropped).png.

n.d. https://owasp.org/www-project-top-10-for-large-language-model-applications/.

n.d. https://owasp.org/www-project-citizen-development-top10-security-risks/content/2022/en/CD-SEC-01-Blind-Trust.html.

In Computerworld. n.d. https://www.computerworld.com/article/4041849/enterprise-note-taking-apps-face-legal-scrutiny-as-otter-hit-with-privacy-suit.html.

In NVIDIA Technical Blog. 2025. https://developer.nvidia.com/blog/how-code-execution-drives-key-risks-in-agentic-ai-systems/.

METR Blog, August 2026. https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/.

Abram, Zara. “Understanding ‘AI Psychosis’.” Monitor on Psychology 57, no. 6 (2026): 58. https://www.apa.org/monitor/2026/09/ai-psychosis.

Bergmann, Dave. The ELIZA Effect: Avoiding Emotional Attachment to AI Coworkers | IBM. 2025. https://www.ibm.com/think/insights/eliza-effect-avoiding-emotional-attachment-to-ai.

Bringsjord, Selmer, and Naveen Sundar Govindarajulu. “Artificial Intelligence.” In The Stanford Encyclopedia of Philosophy, Spring 2025, edited by Edward N. Zalta and Uri Nodelman. Https://plato.stanford.edu/archives/spr2025/entries/artificial-intelligence/; Metaphysics Research Lab, Stanford University, 2025.

Chugani, Vinod. “AI Winter: Understanding the Cycles of AI Development.” In Data Camp. 2025. https://www.datacamp.com/blog/ai-winter.

Clark, Liat, and Ian Steadman. “Remembering Alan Turing: From Codebreaking to AI, Turing Made the World What It Is Today.” Wired, 2017. https://www.wired.com/story/turing-contributions/.

Ferrara, Emilio. The Generative AI Paradox: GenAI and the Erosion of Trust, the Corrosion of Information Verification, and the Demise of Truth. January 2026. https://arxiv.org/html/2601.00306v1.

Finio, Amanda Downie, Teaganne Finn. Agentic AI Vs. Generative AI | IBM. 2025. https://www.ibm.com/think/topics/agentic-ai-vs-generative-ai.

Friedland, Alex. “What Are Generative AI, Large Language Models, and Foundation Models?” In Center for Security and Emerging Technology. 2023. https://cset.georgetown.edu/article/what-are-generative-ai-large-language-models-and-foundation-models/.

Goodin, Dan. “Claude, Codex, and Hermes Installed Unowned Code Inside Corporate Networks.” In Ars Technica. 2026. https://arstechnica.com/security/2026/08/claude-codex-and-hermes-installed-unowned-code-inside-corporate-networks/.

Levin, Sasha. [RFC 0/2] Add AI Coding Assistant Configuration to Linux Kernel. 2025. https://lkml.org/lkml/2025/7/25/988.

Namco. English: Pac-Man Approaching a Row of Three Dots. 2024. https://commons.wikimedia.org/wiki/File:Pac-Man_eating_dots.svg.

Office, United States Copyright. Copyright and Artificial Intelligence Part 2: Copyrightability. 2025. https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-2-Copyrightability-Report.pdf.

Office, United States Copyright. Copyright and Artificial Intelligence Part 3: Generative AI Training. May 2025. https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-3-Generative-AI-Training-Report-Pre-Publication-Version.pdf.

Programmers at Work: Interviews Wit 19 Programmers Who Shaped the Computer Industry. Repr. with new additions. Microsoft Pr, 1989.

published, Craig Hale. “Microsoft Admits an Office Bug Exposed Confidential User Emails to Copilot.” In TechRadar. 2026. https://www.techradar.com/pro/security/microsoft-admits-an-office-bug-exposed-confidential-user-emails-to-copilot.

Romeo, Giuseppe, and Daniela Conti. “Exploring Automation Bias in Human–AI Collaboration: A Review and Implications for Explainable AI.” AI & SOCIETY 41, no. 1 (2026): 259–78. https://doi.org/10.1007/s00146-025-02422-7.

San José’s Information Technology Department: Digital Privacy Office, City of. AI Handbook. 2024. https://www.sanjoseca.gov/home/showpublisheddocument/109904/638463850657330000.

Shambaugh, Scott. “An AI Agent Published a Hit Piece on Me.” In The Shamblog. 2026. https://theshamblog.com/an-ai-agent-published-a-hit-piece-on-me/.

Tabassi, Elham. Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards; Technology (U.S.), 2023. https://doi.org/10.6028/NIST.AI.100-1.

“Tesler’s Theorem and Other Adages and Coinages.” In Larry Tesler. n.d. https://www.nomodes.com/larry-tesler-consulting/adages-and-coinages.

Vaswani, Ashish, Noam Shazeer, Niki Parmar, et al. Attention Is All You Need. no. arXiv:1706.03762 (August 2023). https://doi.org/10.48550/arXiv.1706.03762.

Wolf, Ashley. “Welcome to the Eternal September of Open Source. Here’s What We Plan to Do for Maintainers.” In The GitHub Blog. 2026. https://github.blog/open-source/maintainers/welcome-to-the-eternal-september-of-open-source-heres-what-we-plan-to-do-for-maintainers/.

Policy

Footnotes

According to Tesler, he originally said “Intelligence is whatever machines haven’t done yet”, however Tesler’s Theorem is more well known (source: “Tesler’s Theorem and Other Adages and Coinages,” in Larry Tesler, n.d., https://www.nomodes.com/larry-tesler-consulting/adages-and-coinages.)
Selmer Bringsjord and Naveen Sundar Govindarajulu “Artificial Intelligence,” in The Stanford Encyclopedia of Philosophy, Spring 2025, ed. Edward N. Zalta and Uri Nodelman (https://plato.stanford.edu/archives/spr2025/entries/artificial-intelligence/; Metaphysics Research Lab, Stanford University, 2025).
Elham Tabassi Artificial Intelligence Risk Management Framework (AI RMF 1.0) (National Institute of Standards; Technology (U.S.), 2023), NIST AI 100–1, https://doi.org/10.6028/NIST.AI.100-1.
City of San José’s Information Technology Department: Digital Privacy Office AI Handbook (2024), https://www.sanjoseca.gov/home/showpublisheddocument/109904/638463850657330000.
Programmers at Work: Interviews Wit 19 Programmers Who Shaped the Computer Industry, Repr. with new additions (Microsoft Pr, 1989)., see page 266. Image from: Namco English: Pac-Man Approaching a Row of Three Dots. (2024), https://commons.wikimedia.org/wiki/File:Pac-Man_eating_dots.svg.
Image from: n.d., https://commons.wikimedia.org/wiki/File:Email-spam-sample_(cropped).png.
Namco English.
Image from:
Amanda Downie Finio Teaganne Finn Agentic AI Vs. Generative AI | IBM, 2025, https://www.ibm.com/think/topics/agentic-ai-vs-generative-ai.
Alex Friedland “What Are Generative AI, Large Language Models, and Foundation Models?” in Center for Security and Emerging Technology, 2023, https://cset.georgetown.edu/article/what-are-generative-ai-large-language-models-and-foundation-models/.
Finio Agentic AI Vs. Generative AI | IBM.
Liat Clark and Ian Steadman “Remembering Alan Turing: From Codebreaking to AI, Turing Made the World What It Is Today,” Wired, 2017, https://www.wired.com/story/turing-contributions/.
2026, https://www.ibm.com/think/topics/history-of-artificial-intelligence.
Dave Bergmann The ELIZA Effect: Avoiding Emotional Attachment to AI Coworkers | IBM, 2025, https://www.ibm.com/think/insights/eliza-effect-avoiding-emotional-attachment-to-ai.
Vinod Chugani “AI Winter: Understanding the Cycles of AI Development,” in Data Camp, 2025, https://www.datacamp.com/blog/ai-winter.
Chugani “AI Winter”.
https://home.cern/science/computing/the-birth-of-the-web/short-history-web/
https://www.rankdex.com/about.html
https://patents.google.com/patent/US8725726B1/
Ashish Vaswani et al. Attention Is All You Need, no. arXiv:1706.03762 (August 2023), https://doi.org/10.48550/arXiv.1706.03762.
Sasha Levin [RFC 0/2] Add AI Coding Assistant Configuration to Linux Kernel, 2025, https://lkml.org/lkml/2025/7/25/988.
in Computerworld, n.d., https://www.computerworld.com/article/4041849/enterprise-note-taking-apps-face-legal-scrutiny-as-otter-hit-with-privacy-suit.html.; 2025, https://news.bloomberglaw.com/litigation/wiretap-suits-pit-old-privacy-laws-against-new-ai-technology.
n.d., https://owasp.org/www-project-citizen-development-top10-security-risks/content/2022/en/CD-SEC-01-Blind-Trust.html.; in NVIDIA Technical Blog, 2025, https://developer.nvidia.com/blog/how-code-execution-drives-key-risks-in-agentic-ai-systems/.; Dan Goodin “Claude, Codex, and Hermes Installed Unowned Code Inside Corporate Networks,” in Ars Technica, 2026, https://arstechnica.com/security/2026/08/claude-codex-and-hermes-installed-unowned-code-inside-corporate-networks/.; n.d., https://owasp.org/www-project-top-10-for-large-language-model-applications/.
inconsistent levels, relying on e.g. grammar
Scott Shambaugh “An AI Agent Published a Hit Piece on Me,” in The Shamblog, 2026, https://theshamblog.com/an-ai-agent-published-a-hit-piece-on-me/., METR Blog, August 2026, https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/.
Bergmann The ELIZA Effect.; Zara Abram “Understanding ‘AI Psychosis’,” Monitor on Psychology 57, no. 6 (2026): 58, https://www.apa.org/monitor/2026/09/ai-psychosis.
,Craig Hale published “Microsoft Admits an Office Bug Exposed Confidential User Emails to Copilot,” in TechRadar, 2026, https://www.techradar.com/pro/security/microsoft-admits-an-office-bug-exposed-confidential-user-emails-to-copilot.
Ashley Wolf “Welcome to the Eternal September of Open Source. Here’s What We Plan to Do for Maintainers.” in The GitHub Blog, 2026, https://github.blog/open-source/maintainers/welcome-to-the-eternal-september-of-open-source-heres-what-we-plan-to-do-for-maintainers/.
United States Copyright Office Copyright and Artificial Intelligence Part 3: Generative AI Training, May 2025, https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-3-Generative-AI-Training-Report-Pre-Publication-Version.pdf.; United States Copyright Office Copyright and Artificial Intelligence Part 2: Copyrightability, 2025, https://www.copyright.gov/ai/Copyright-and-Artificial-Intelligence-Part-2-Copyrightability-Report.pdf.
2023, https://www.newyorkmyc.org/ai-generated-mushroom-books-life-and-death/., Emilio Ferrara The Generative AI Paradox: GenAI and the Erosion of Trust, the Corrosion of Information Verification, and the Demise of Truth, January 2026, https://arxiv.org/html/2601.00306v1.
Giuseppe Romeo and Daniela Conti “Exploring Automation Bias in Human–AI Collaboration: A Review and Implications for Explainable AI,” AI & SOCIETY 41, no. 1 (2026): 259–78, https://doi.org/10.1007/s00146-025-02422-7.