AI Ethics with Professor Casey

bit.ly/ai-ethics-syllabus

For nearly five years, I've (Casey Fiesler aka Professor Casey) been creating social media content (largely on TikTok and Instagram) about artificial intelligence, especially as related to ethics, policy, and social impact. This page is intended as a syllabus of sorts--a post-hoc curated collection of videos that provide introductions, examples, and deep dives into various concepts.

In addition to the resources below, I also maintain a spreadsheet of AI ethics and policy news that is categorized into similar buckets, for further reading.

🚧 This document is a work in progress! Though I’m officially sharing it as of June 2025 with a skeleton of some favorite videos, it will continue to be populated with both old and new content, and I will also continue to add many more references to further readings and resources from others.

I am also in the process of uploading all of these videos to my YouTube channel as well. Some links will initially only be to TikTok because that’s where they were originally shared but more videos will be populated with YouTube links! And new videos that I create will be posted to Instagram as well.

If you have any questions or comments, please email casey.fiesler@colorado.edu!

Table of Contents

1  What is AI and what can it do?

2  Information literacy and mis/disinformation

3  Bias and fairness

4  IP and data

5  Labor and creativity

6  Social Media and social AI

7  Education & Learning

8  Sustainability

9  Regulation

10  Risk and resistance

🤖 What is AI and what can it do?

In 1950, computer scientist Alan Turing proposed "the imitation game," which became known as the Turing Test. It was meant as a way to test a machine's ability to display intelligence like a human. Since then, what we refer to as “artificial intelligence” continues to evolve - an exact definition is a challenge even for experts! Here you’ll find references to all manner of “AI” in which a machine is performing a task we’d typically think of as the sort of thing that requires human intelligence - from recommendation algorithms to predictive machine learning to large language models to robots.

🎥Origin of the term “artificial intelligence” [YouTube] [TikTok]

July 13, 2025

The term “artificial intelligence” first appeared in the proposal for the 1956 summer workshop at Dartmouth University that brought together researchers like Marvin Minsky, Claude Shannon, John McCarthy, and Nathaniel Rochester. This workshop has been widely considered as the origin point of the AI research field.

🎥 update that further explains narrow vs general AI (February 23, 2026) [TikTok]

McCarthy, John; Minsky, Marvin; Rochester, Nathan; Shannon, Claude (1955), A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.

Solomonoff, Grace (2023-05-06). "The Meeting of the Minds That Launched AI." IEEE Spectrum.

🎥The Turing Test [YouTube] [TikTok] 

April 11, 2024

The Turing Test framed the question of “Can machines think?” as an “imitation game”--whether a human could converse with a machine without realizing it was a machine. The original game also involved gender impersonation, which Roboticist Ayanna Howard highlighted raises questions about human bias as well.

Turing, Alan. Computing Machinery and Intelligence. Mind: 433-460. (1950.)

The Turing Test (Stanford Encyclopedia of Philosophy, revised 2021).

Ayanna Howard Discusses Robotics and Unconscious Bias. Grace Hopper Celebration: YouTube. (2018.)

🎥Can Machines Think? [TikTok]

January 19, 2026

Exploring the question “can machines think?” from Turing’s pragmatic test to a more philosophical exploration (re: symbolic AI) of Searle’s Chinese Room thought experiment.

🎥 Follow-up re: the “deep sea octopus” thought experiment [TikTok]

The Chinese Room Argument (Stanford Encyclopedia of Philosophy)

Bender, Emily M., and Alexander Koller. "Climbing towards NLU: On meaning, form, and understanding in the age of data." Proceedings of the 58th annual meeting of the association for computational linguistics. 2020.

🎥The OG chatbot ELIZA [YouTube] [TikTok]

December 15, 2022

In 1966, computer scientist Joseph Weizenbaum introduced ELIZA, one of the first computer programs to simulate human conversation. Using simple pattern matching and substitution rules, ELIZA’ could mimic a psychotherapist by reflecting users’ statements back to them as questions.

[Discussion of ELIZA also continues below in the section on Social Media & Social AI.]

Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45.

🎥Generative AI vs Machine Learning [YouTube] [TikTok]

April 8, 2024

Generative AI refers to machine learning models that can create new content (like text, images, audio, or video) based on patterns learned from large datasets. Examples include ChatGPT, which generates human-like text, and Midjourney, which produces images from text prompts. These systems are a subset of machine learning, in which models recognize patterns, make predictions, or classify data.

A Very Gentle Introduction to Large Language Models without the Hype (by Mark Riedl, 2023).

🎥Bias in Machine Learning [TikTok] [YouTube]

February 26, 2022

Based on how machine learning works, a simplistic explanation of how bias can happen.

🎥What is a large language model (LLM)? [YouTube] [TikTok]

April 25, 2024

Explaining it like you’re five!

🎥How does a language model work? [YouTube Playlist]

Part 1 (February 23, 2026): The Basics [TikTok] [YouTube]

Part 2 (February 28, 2026): Self-Supervised Learning [TikTok] [YouTube]

Part 3 (March 2, 2026): Vector Space and Neural Networks [TikTok] [YouTube]

Part 4 (March 4, 2026): Self Attention [TikTok] [YouTube]

Interlude (March 5, 2026): Why You Should Learn About This [TikTok] [YouTube]

Part 5 (March 7, 2026): Transformers [TikTok] [YouTube]

Part 6 (March 11, 2026): Emergence [TikTok] [YouTube]

Part 7 (March 14, 2026): Instruction Tuning [TikTok] [YouTube]

Part 8 (March 17, 2026): Reinforcement Learning [TikTok] [YouTube]

Part 9 (March 19, 2026): Context Window [TikTok] [YouTube]

Part 10 (March 21, 2026): Hallucinations [TikTok] [YouTube]

Part 11 (March 23, 2026): Retrieval Augmented Generation [TikTok] [YouTube]

Part 12 (April 1, 2026): Tokens [TikTok] [YouTube]

Interlude (April 2, 2026: How Many R’s in Strawberry [TikTok] [YouTube]

Part 13 (June 6, 2026): Chain of Thought [TikTok] [YouTube]

Part 14 (June 12, 2026): Prompts [TikTok] [YouTube]

Part 14

This is a series of videos that attempt to explain how LLMs work without hype and without over-simplification. Something between “they’re just a next word predictor!” and “now you’ve taken a machine learning class!” They are also heavily informed/inspired by this series of blog posts by AI researcher Mark Riedl:

A Very Gentle Introduction to Large Language Models without the Hype (2023)

The Intuition Behind How Large Language Models Work Part 1 (2025)

The Intuition Behind How Large Language Models Work Part 2 (2025)

Additional citations:

King - Man + Woman = Queen: The Marvelous Mathematics of Computational Linguistics (MIT Technology Review, 2015)

Vaswani, Ashish, et al. "Attention is all you need." Advances in Neural Information Processing Systems 30 (2017).

Wei, Jason, et al. "Emergent abilities of large language models." arXiv preprint arXiv:2206.07682 (2022).

Schaeffer, Rylan, Brando Miranda, and Sanmi Koyejo. "Are emergent abilities of large language models a mirage?" Advances in Neural Information Processing Systems (NeurIPS) 36 (2023): 55565-55581.

🎥What is artificial general intelligence (AGI)? [YouTube] [TikTok]

June 12, 2025

In contrast to “narrow” AI, artificial general intelligence typically refers to AI that has human-like abilities across all types of tasks.

Meredith Ringel Morris, Jascha Sohl-Dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clement Farabet, and Shane Legg. 2024. Levels of AGI for operationalizing progress on the path to AGI. In Proceedings of the 41st International Conference on Machine Learning (ICML'24).

🎥 Why ChatGPT is not a search engine [YouTube] [TikTok]

June 20, 2025

Large language models like ChatGPT don’t work by searching for information over their training data. Remember that they are designed to be statistically probably and linguistically fluent, not verifiably accurate!

 🎥Follow-up re: AI-powered search engines can still hallucinate [YouTube] [TikTok]

Jazwinska, Klaudia and Aisvarya Chandrasekar. AI Search Has a Citation Problem. Columbia Journalism Review. (6 March 2025).

🎥 Why ChatGPT can’t tell time [YouTube] [TikTok]

June 20, 2025

Sam Altman was asked in a podcast interview to react to a viral video from TikTokker Husk in which he ChatGPT in voice mode had “lied” about its ability to time a run. The example demonstrates a technical challenge with (non-)use of tool calling, but also Altman’s response suggested a misunderstanding of the more important problem–the “lying.”

Sam Altman Says It’ll Take Another Year Before ChatGPT Can Start a Timer (Gizmodo, April 2026)

🎥 The challenge of image models [YouTube] [TikTok]

April 4, 2026

Why does an image model like the one connected to ChatGPT struggle with text (using the example of visual alphabets)?

🎥 Why you can’t just ask an LLM to not hallucinate [YouTube] [TikTok]

July 22, 2025

Even when explicitly prompting ChatGPT to only include reliable, verified searches, and even when it conducts a web search, it might still fabricate information and citations.

Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu. 2025. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions. ACM Trans. Inf. Syst. 43, 2, Article 42 (March 2025), 55 pages.

A.I. Is Getting More Powerful, but Its Hallucinations Are Getting Worse (The New York Times, May 2025)

🎥 ChatGPT (Year One) Wrapped [TikTok]

November 30, 2023

A year of ChatGPT in the news.

ChatGPT Wrapped (An AI’s Year in Review) (by Casey Fiesler, 2023).

🎥 A Taxonomy of Risks of Large Language Models

Part 1 (September 20, 2024): Discrimination, Hate Speech, and Exclusion (Bias) [YouTube] [TikTok]

Part 2 (October 2, 2024): Information Hazards (Privacy) [YouTube] [TikTok]

Part 3 (July 27, 2025): Misinformation [YouTube] [TikTok]

Part 4 (November 16, 2025): Malicious Uses [TikTok]

Part 5 (November 17, 2025): Human Interaction Harms [TikTok]

Part 6 (December 2, 2025): Environmental Impact) [YouTube] [TikTok]

Part 7 (December 20, 2025): Socioeconomic Harms) [YouTube] [TikTok]

The 2022 paper “Taxonomy of Risks Posed by Language Models” laid out six categories of actual and anticipated risks. This series of videos maps those (pre-ChatGPT) risks to actual harms that have occurred between 2023 and 2025.

Weidinger, Laura, et al. "Taxonomy of risks posed by language models." Proceedings of the ACM FAccT Conference on Fairness, Accountability, and Transparency. 2022.

Discrimination & Exclusion

Information Harms/Privacy

Misinformation

Malicious Uses

Human Interaction Harms

Environmental Impacts

Socioeconomic Harm

📚 Further Reading

AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How To Tell the Difference by Arvind Narayanan and Sayash Kapoor (Princeton University Press, 2024)

A Very Gentle Introduction to Large Language Models without the Hype (by Mark Riedl, 2023).

Hagendorff, Thilo, and Katharina Wezel. "15 challenges for AI: or what AI (currently) can’t do." Ai & Society 35.2 (2020): 355-365.

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the dangers of stochastic parrots: Can language models be too big?🦜. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610-623).

Krafft, P. M., et al. "Defining AI in policy versus practice." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 2020.

Anjali Khurana, Hariharan Subramonyam, and Parmit K Chilana. 2024. Why and When LLM-Based Assistants Can Go Wrong: Investigating the Effectiveness of Prompt-Based Interactions for Software Help-Seeking. In Proceedings of the 29th International Conference on Intelligent User Interfaces (IUI '24).

Long, Duri, and Brian Magerko. "What is AI literacy? Competencies and design considerations." Proceedings of the 2020 CHI conference on human factors in computing systems. 2020.

Rehak, Rainer. "AI Narrative Breakdown. A Critical Assessment of Power and Promise." In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, pp. 1250-1260. 2025.

📊 Information literacy and mis/disinformation

Misinformation

As you know from the content above, systems like ChatGPT are not information retrieval systems, and sometimes they can provide incorrect information. As a result, unintentional misinformation has been a significant problem as LLMs are increasingly integrated into all sorts of contexts. Here are some examples.

🎥 Lawyers Using ChatGPT [YouTube] [TikTok]

May 30, 2023

Lawyers are being fined and censured when they file court documents that include citations to court decisions that didn’t exist. There are both technical and human problems associated with generative AI hallucinations.

🎥 Follow-up re: the sociotechnical problem of hallucinations (2025) [YouTube] [TikTok]

ChatGPT: US lawyer admits using AI for case research (BBC, 2023).

A “Brief” Hallucination by Generative AI Can Land You in Hot Water (The National Law Review, 2023).

The ChatGPT Lawyer Explains Himself (The New York Times, 2023)

Judge Fines Lawyers for MyPillow Founder for Error-Filled Court Filing (The New York Times, July 2025)

🎥 Harmful Helpline Chatbot [YouTube] [TikTok]

May 31, 2023

CW: eating disorders

Eating Disorder Helpline Fires Staff, Transitions to Chatbot After Unionization (Vice, 2023)

Eating Disorder Helpline Disables Chatbot for ‘Harmful’ Responses After Firing Human Staff (Vice, 2023)

An eating disorders chatbot offered dieting advice, raising fears about AI in health (NPR, 2023)

🎥 NYC Business Chatbot [YouTube] [TikTok]

March 29, 2024

NYC’s AI Chatbot Tells Businesses to Break the Law (The Markup, 2024)

🎥 Chatbot Pretends to be a Parent [TikTok]

April 18, 2024

Facebook’s AI Told Parents Group It Has a Gifted, Disabled Child (404 Media, 2024)

AI chatbots are intruding into online communities where people are trying to connect with other humans (by Casey Fiesler, The Conversation, 2024).

Disinformation

In addition to unintentional misinformation, generative AI has also made intentional disinformation more convincing as well as much easier to produce and therefore more widespread.

🎥 Talk to your loved ones about AI [TikTok] 

December 23, 2023

Particularly re: mis/disinformation and scams.

🎥 Social media and AI scams [TikTok] [YouTube]

March 25, 2024

AI-generated garbage overtaking Facebook seems to be an especially significant problem with respect to older adults.

Facebook Is Filled With AI-Generated Garbage—and Older Adults Are Being Tricked (The Daily Beast, 2024t)

How Spammers, Scammers and Creators Leverage AI-Generated Images on Facebook for Audience Growth (Stanford Internet Observatory)

🎥 AI-enabled scam ads [YouTube] [TikTok] 

March 10, 2024

Don’t fall for these!

🎥 AI-generated “true” crime videos [YouTube] [TikTok]

August 27, 2024

Platforms like YouTube are full of AI-generated misinformation, including this particular genre of (not)true-crime. Always check the source of information before you believe it!

A "true crime" video about a Littleton man's "secret gay love affair" with his murderous stepson is going viral. It's fake. (The Denver Post, August 2024)

Information Literacy and Disbelief

As disinformation becomes more and more convincing, it is really important that we are able to determine what’s real and what’s not - which often involves evaluating the credibility of the source of information. And this same thing is true when we disbelieve something because we think it might be fake; another risk of AI is “plausible deniability of reality” (sometimes called the liar’s dividend).

🎥 It’s easy to lie on the internet [TikTok]

September 3, 2023

If so many people can believe a photoshopped fake tweet, what hope do we have for convincing deepfakes? Verify the source of information!

Fake social media posts on Burning Man festival stir conspiracy theory frenzy (The Guardian, 2023)

🎥 The liar’s dividend [TikTok] 

May 28, 2025

The liar’s dividend is the benefit that bad actors get from the existence of deepfakes, in how easy it becomes to deny the truth.

Chesney, Bobby, and Danielle Citron. "Deep fakes: A looming challenge for privacy, democracy, and national security." California Law Review 107 (2019): 1753.

Real TikTokers are pretending to be Veo 3 AI creations for fun, attention (Ars Technica, 2025)

🎥 AI and disbelief: Catherine Middleton [TikTok]

March 23, 2023

Princess Catherine cancer video spawns fresh round of AI conspiracies (The Washington Post)

🎥 AI and disbelief: Kamala Harry rally [TikTok]

April 12, 2024

Why false claims that a picture of a Kamala Harris rally was AI-generated matter (NPR, 2024)

📚 Further Reading

Carl Bergstrom and Jevin West. Calling Bullshit: The Art of Skepticism in a Data-Driven World. (Penguin Random House, 2021).

Chesney, Bobby, and Danielle Citron. "Deep fakes: A looming challenge for privacy, democracy, and national security." California Law Review 107 (2019): 1753.

Jim Waldo and Soline Boussard. 2024. GPTs and Hallucination: Why do large language models hallucinate? Queue 22, 4, Pages 10 (July/August 2024), 15 pages.

Katrin Hartwig, Frederic Doell, and Christian Reuter. 2024. The Landscape of User-centered Misinformation Interventions - A Systematic Literature Review. ACM Comput. Surv. 56, 11, Article 292 (November 2024).

Jazwinska, Klaudia and Aisvarya Chandrasekar. AI Search Has a Citation Problem. Columbia Journalism Review. (6 March 2025).

Wang, Tianyi, Xin Liao, Kam Pui Chow, Xiaodong Lin, and Yinglong Wang. 2024. Deepfake Detection: A Comprehensive Survey from the Reliability Perspective. ACM Comput. Surv. Just Accepted (October 2024).

Rathje, S., Roozenbeek, J., Van Bavel, J. J., & van der Linden, S. (2023). Accuracy and social motivations shape judgements of (mis) information. Nature Human Behaviour, 1-12.

Brett A. Halperin and Stephanie M Lukin. 2024. Artificial Dreams: Surreal Visual Storytelling as Inquiry Into AI 'Hallucination'. In Proceedings of the 2024 ACM Designing Interactive Systems Conference (DIS '24).

Connie Moon Sehat, Ryan Li, Peipei Nie, Tarunima Prabhakar, and Amy X. Zhang. 2024. Misinformation as a Harm: Structured Approaches for Fact-Checking Prioritization. Proc. ACM Hum.-Comput. Interact. 8, CSCW1, Article 171 (April 2024).

Ayoobi, Navid, Sadat Shahriar, and Arjun Mukherjee. "Seeing Through AI's Lens: Enhancing Human Skepticism Towards LLM-Generated Fake News." In Proceedings of the 35th ACM Conference on Hypertext and Social Media, pp. 1-11. 2024.

👤 Bias & Fairness

AI systems can reflect or amplify societal biases present in their training data or design, often leading to unfair or discriminatory outcomes. Fairness in AI involves identifying, evaluating, and mitigating these biases.. It also requires ongoing reflection on whose values are being encoded and prioritized in these systems.

🎥 The AI bias before Christmas [Tiktok] [YouTube (long form!)]

December 17, 2022

Explaining bias in machine learning like you’re five–with something five-year-olds care about… Santa.

🎥 Barbies & bias [TikTok] [YouTube]

July 10, 2023

AI-generated Barbies from ‘every country’ blasted on Twitter for racism (Business Inside, July 2023).

How AI reduces the world to stereotypes (Rest of the World, October 2023).

🎥 Two types of AI bias [TikTok] [YouTube]

December 20, 2024

One way that bias can make its way into AI systems is through unequal representation in training data. One possible consequence of this problem is unequal performance across a task, but another is the proliferation of stereotypes.

🎥 Bias Mitigation in DALL-E [TikTok]

March 26, 2023

In the early days of OpenAI’s image generator DALL-E, the company explained an explicit bias mitigation technique that they implemented.

Reducing bias and improving safety in DALL-E 2 (OpenAI, July 2022)

AI art tool DALL-E 2 adds 'black' or 'female' to some image prompts (New Scientist, July 2022)

🎥 Bias in facial recognition [TikTok] [YouTube]

December 15, 2022

A social media app just for 'females' intentionally excludes trans women — and some say its face-recognition AI discriminates against women of color, too (Business Insider, 2022)

Buolamwini, Joy, and Timnit Gebru. "Gender shades: Intersectional accuracy disparities in commercial gender classification." In Conference on fairness, accountability and transparency, pp. 77-91. PMLR, 2018.

🎥 Joy Buolamwini’s Unmasking AI [TikTok]

October 31, 2023

Buolamwini, Joy. Unmasking AI: My mission to protect what is human in a world of machines. Random House, 2024.

https://www.unmasking.ai/ 

🎥 Racial bias in face detection [TikTok]

January 1, 2021

Are Face-Detection Cameras Racist? (Time, 2010)

Google engineer apologizes after Photos app tags two black people as gorillas (The Verge, 2015)

🎥 Search engine bias and band-aid solutions [TikTok]

July 14, 2021

Noble, Safiya Umoja. Algorithms of oppression: How search engines reinforce racism. New York University Press, 2018.

How TikTok’s hate speech detection tool set off a debate about racial bias on the app (Vox, 2021)

Google's solution to accidental algorithmic racism: ban gorillas (The Guardian, 2018)

🎥 Racial bias in healthcare algorithms [TikTok]

July 8, 2021

A hospital algorithm designed to predict a deadly condition misses most cases (The Verge, 2021).

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.

🎥 LLM racial bias in medicine [YouTube] [TikTok]

October 27, 2023

Omiye, J. A., Lester, J. C., Spichak, S., Rotemberg, V., & Daneshjou, R. (2023). Large language models propagate race-based medicine. NPJ Digital Medicine, 6(1), 195.

🎥 Covert racial bias in LLMs [YouTube] [TikTok]

March 7, 2024

Hofmann, V., Kalluri, P. R., Jurafsky, D., & King, S. (2024). Dialect prejudice predicts AI decisions about people's character, employability, and criminality. arXiv preprint arXiv:2403.00742. (Pre-Print.)

📚 Further Reading

Asplund, Joshua, Motahhare Eslami, Hari Sundaram, Christian Sandvig, and Karrie Karahalios. "Auditing race and gender discrimination in online housing markets." In Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 24-35. 2020.

Buolamwini, Joy. Unmasking AI: My mission to protect what is human in a world of machines. Random House, 2024.

Buolamwini, Joy, and Timnit Gebru. "Gender shades: Intersectional accuracy disparities in commercial gender classification." In Conference on fairness, accountability and transparency, pp. 77-91. PMLR, 2018.

Benjamin, Ruha. Race After Technology: Abolitionist Tools for the New Jim Code. Polity Press. 2019.

Scheuerman, Morgan Klaus, Jacob M. Paul, and Jed R. Brubaker. "How computers see gender: An evaluation of gender classification in commercial facial analysis services." Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-33.

Lerman, Jonas. 2013.  Big Data and Its Exclusions, Stanford Law Review

Chouldechova, Alexandra, and Aaron Roth. "A snapshot of the frontiers of fairness in machine learning." Communications of the ACM 63, no. 5 (2020): 82-89.

Hoffmann, Anna Lauren. "Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse." Information, Communication & Society 22, no. 7 (2019): 900-915.

Obermeyer, Ziad, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. "Dissecting racial bias in an algorithm used to manage the health of populations." Science 366, no. 6464 (2019): 447-453.

Raji, Inioluwa Deborah, and Joy Buolamwini. "Actionable Auditing Revisited: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products." Communications of the ACM 66, no. 1 (2022): 101-108.

Selbst, Andrew D., danah boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. "Fairness and abstraction in sociotechnical systems." In Proceedings of the conference on fairness, accountability, and transparency, pp. 59-68. 2019.

Yee, Kyra, Uthaipon Tantipongpipat, and Shubhanshu Mishra. "Image cropping on twitter: fairness metrics, their limitations, and the importance of representation, design, and agency." Proceedings of the ACM on Human-Computer Interaction 5, no. CSCW2 (2021): 1-24.

©️ IP & Data

Many AI systems rely heavily on vast datasets, many of which are constructed through the collection of content (from published books to social media posts) without the knowledge or consent of the original creators. This raises complex questions around intellectual property (particularly copyright), ownership, and the ethical use of publicly available data.

🎥 AI & IP Overview [TikTok] [YouTube]

January 23, 2024

There are three major issues regarding especially copyright and generative AI: is AI training fair use, is AI output copyrightable, and is AI output infringing?

🎥 U.S. Copyright Office on Training Data and Fair Use [TikTok] [YouTube]

May 19, 2025

The U.S. Copyright Office put out a report in May 2025 laying out a prima facie case for using copyrighted content for training generative AI systems, as well as fair use arguments.

Copyright and Artificial Intelligence Part 3: Generative AI Training (U.S. Copyright Office, May 2025)

🎥 Do LLMs “memorize” copyrighted content? [TikTok]

June 9, 2025

Cooper, A. Feder, Aaron Gokaslan, Amy B. Cyphert, Christopher De Sa, Mark A. Lemley, Daniel E. Ho, and Percy Liang. "Extracting memorized pieces of (copyrighted) books from open-weight language models." arXiv preprint arXiv:2505.12546 (2025).

🎥 Can You Copyright Your Face? [TikTok] [YouTube]

July 5, 2026

Can you copyright your face? Or your voice? Not really, though Denmark is kind of trying anyway. It also makes sense why this would help with deepfakes and AI impersonation, but copyright (as opposed to publicity or personality rights or privacy or even trademark) is probably not the box you want to stick a human into.

Rothman, J. E. (2025). Copyrighting People. Journal of Copyright Society, 72(1), 25-07.

Qiwei, L., Zhang, S., Kasper, A. T., Ashkinaze, J., Eaton, A. A., Schoenebeck, S., & Gilbert, E. Reporting non-consensual intimate media: An audit study of deepfakes. Forthcoming, Proceedings of the ACM on Human-Computer Interaction.

📚 Further Reading

Copyright and Artificial Intelligence Part 3: Generative AI Training (U.S. Copyright Office, May 2025)

Grimmelmann, James. "There's No Such Thing as a Computer-Authored Work-And It's a Good Thing, Too." Colum. JL & Arts 39 (2015): 403.

Guadamuz, Andres. "Do androids dream of electric copyright? Comparative analysis of originality in artificial intelligence generated works." Intellectual property quarterly (2017).

Levendowski, Amanda. "How copyright law can fix artificial intelligence's implicit bias problem." Washington Law Review. 93 (2018): 579.

Levendowski, Amanda. "Resisting Face Surveillance with Copyright Law." North Carolina Law Review. 100 (2021): 1015.

Mantegna, Micaela. "ARTificial: Why Copyright Is Not the Right Policy Tool to Deal with Generative AI." Yale Law Journal Forum 133 (2023): 1126.

🖌️ Labor and Creativity

The rise of generative AI systems in work and creative contexts has resulted in the automation of some tasks that once required human skill, raising serious concerns about job displacement. Meanwhile, writers, artists, musicians, and other creators are threatened by the devaluing of creative work. These issues are a reflection of broader economic structures and systems of power that often prioritize scalability and profit over sustainable labor and autonomy. As AI transforms the future of work, it is important to consider who benefits from such transformation.

🎥 Who benefits from AI productivity gains? [TikTok] [YouTube]

June 4, 2025

A statistic about job cuts and productivity expectations is a good reminder to think about who benefits most from AI in the workplace.

At Amazon, Some Coders Say Their Jobs Have Begun to Resemble Warehouse Work (The New York Times, 2025)

🎥 Lessons from the Luddites [TikTok] [YouTube]

June 5, 2025

The Luddites weren’t anti-technology; they were anti-worker-exploitation.

Merchant, Brian. Blood in the Machine: The Origins of the Rebellion Against Big Tech. Hachette Book Group, 2023.

What the Luddites Can Teach Us About Artificial Intelligence (TIME, 2023)

🎥 Labor and domestic technology [TikTok] [YouTube]

June 6, 2025

According to Cowan’s book, the washing machine did not decrease labor; it redistributed it, which has some interesting parallels to AI today.

Cowan, Ruth Schwartz. (1985). More work for mother: The ironies of household technology from the open hearth to the microwave. Plunkett Lake Press.

🎥 Tokenmaxxing and Productivity Theater [TikTok] [YouTube]

April 26, 2026

News of lay-offs at Meta in 2026 connects to earlier reports of employee incentives to maximize AI use and an internal leaderboard for “tokenmaxxing,” a signalling strategy that has become common across the tech industry and also harkens back to very early management principles around measurement.

🎥 Follow-up re: “what gets measured” and scientific management [TikTok] [YouTube]

Taylor, F. W. (1911). The Principles of Scientific Management. NuVision Publications, LLC.

Meta to cut one in 10 jobs after spending billions on AI (BBC News, April 2026)

Meta links employee AI usage to performance reviews & rewards (HR Grapevine, February 2026)

Meta to start capturing employee mouse movements, keystrokes for AI training data (Reuters, April 2026)

A Meta employee created a dashboard so coworkers can compete to be the company’s No. 1 AI token user (Fortune, April 2026)

More! More! More! Tech Workers Max Out Their A.I. Use. (The New York Times, March 2026)

🎥 SAG-AFTRA agreement [TikTok] [YouTube]

November 13, 2023

As part of the agreements that ended the 2023 actors strike, this AI agreement largely considered aspects of consent and compensation regarding e.g. likeness use.

SAG-AFTRA AI Resources

2023 SAG-AFTRA strike (Wikipedia)

🎥 Artist concerns about generative AI [TikTok] [YouTube]

April 24, 2024

The reaction to a tweet from William Shatner about AI-generated artwork provided a good example of how artist concerns around AI aren’t just about copyright.

William Shatner faces backlash for using AI art cover on new music album (VentureBeat, 2024)

📚 Further Reading

Armstrong, Lena, and Danaé Metaxa. "Navigating Automated Hiring: Perceptions, Strategy Use, and Outcomes Among Young Job Seekers." Proceedings of the ACM on Human-Computer Interaction 9, no. 2 (2025): 1-26.

Bennett, Cynthia L., Renee Shelby, Negar Rostamzadeh, and Shaun K. Kane. "Painting with Cameras and Drawing with Text: AI Use in Accessible Creativity." In Proceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 1-19. 2024.

Chen, Ni, Zhi Li, and Bo Tang. "Can digital skill protect against job displacement risk caused by artificial intelligence? Empirical evidence from 701 detailed occupations." PLoS One 17, no. 11 (2022): e0277280.

Gero, Katy Ilonka, Meera Desai, Carly Schnitzler, Nayun Eom, Jack Cushman, and Elena L. Glassman. "Creative Writers' Attitudes on Writing as Training Data for Large Language Models." In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, pp. 1-16. 2025.

Jiang, Harry H., Lauren Brown, Jessica Cheng, Mehtab Khan, Abhishek Gupta, Deja Workman, Alex Hanna, Johnathan Flowers, and Timnit Gebru. 2023. AI Art and its Impact on Artists. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (AIES '23).

Taylor, Jordan, Joel Mire, Franchesca Spektor, Alicia DeVrio, Maarten Sap, Haiyi Zhu, and Sarah E. Fox. "Un-Straightening Generative AI: How Queer Artists Surface and Challenge Model Normativity." In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, pp. 951-963. 2025.

Goetze, Trystan S. "AI art is theft: Labour, extraction, and exploitation: Or, on the dangers of stochastic Pollocks." In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, pp. 186-196. 2024.

Tomlinson, Kiran, Sonia Jaffe, Will Wang, Scott Counts, and Siddharth Suri. "Working with AI: Measuring the Occupational Implications of Generative AI." arXiv preprint arXiv:2507.07935 (2025). [Pre-print, not yet peer reviewed]

Kawakami, Anna, Jordan Taylor, Sarah Fox, Haiyi Zhu, and Ken Holstein. "Ai failure loops in feminized labor: Understanding the interplay of workplace ai and occupational devaluation." In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, vol. 7, pp. 683-683. 2024.

Ma, S., Liu, Z., Nisi, V., Fox, S. E., & Nunes, N. J. (2025, April). Speculative Job Design: Probing Alternative Opportunities for Gig Workers in an Automated Future. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1-18).

👥 Social Media & Social AI

AI-driven systems shape much of our social and cultural life, from the recommendation algorithms that curate what we see on social media to conversational agents that may be mediating–or even replacing–other forms of communication. Algorithms have the potential to shape social norms in ways that privilege engagement over well-being and to shape what voices are heard and what topics we see. Additionally, companion chatbots and social AI raise new questions about intimacy, dependence, and how AI might impact human relationships.  Understanding the role of social AI requires grappling with both its capacity to connect and its power to manipulate, as well its potential to either support or replace human connections.

Social Media Algorithms

Social media recommendation systems use predictive machine learning to both reflect and actively shape our interests, designed to steer attention towards content that maximizes engagement rather than wellbeing.

🎥 Social Media Algorithms & Harmful Rabbit Holes [YouTube] [TikTok]

July 22, 2021

In 2021 an investigative report got a lot of attention for revealing how the TikTok recommendation algorithm can send users quickly into harmful rabbit holes. Conclusions about niche content also harken back to research about harmful content on Instagram.

Inside TikTok’s Algorithm: A WSJ Video Investigation (Wall Street Journal, July 2021)

Chancellor, S., Pater, J. A., Clear, T., Gilbert, E., & De Choudhury, M. (2016). #thyghgapp: Instagram content moderation and lexical variation in pro-eating disorder communities. In Proceedings of the ACM Conference on Computer-Supported Cooperative Work & Social Computing (pp. 1201-1213).

Companion Chatbots

Companion chatbots, or even all-purpose language models used for conversational purposes, can create a blurry line between technology and relationship. This AI use case raises concerns about emotional dependence, data exploitation, and even how we experience grief.

🎥The “ELIZA” Effect [TikTok] [YouTube]

March 8, 2026

Joseph Weizenbaum’s 1966 chatbot (explained above in the video “the OG chatbot ELIZA”) simulates human conversation using simple pattern matching and substitution rules. Although its responses were superficial, many users still perceived ELIZA as understanding them and responded socially to it, a reaction that surprised and concerned Weizenbaum who later became an outspoken critic of AI applications that could deceive or manipulate people.

Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45.

Weizenbaum, J. (1976). Computer power and human reason: From judgment to calculation. W.H. Freeman and Company.

Weizenbaum’s nightmares: how the inventor of the first chatbot turned against AI (The Guardian, June 2023)

🎥Computers Are Social Actors [TikTok] [YouTube]

March 8, 2026

It’s not surprising at all that people perceive chatbots socially–even the simplistic ELIZA (see above) from the 1960s. We are pre-disposed to respond to machines to machines socially, especially when their interaction style is human language, which is also something that can be exploited.

Nass, C., Steuer, J., & Tauber, E. R. (1994). Computers are social actors. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 72-78).

🎥 AI end-of-life [YouTube] [TikTok]

August 9, 2025

Technologies, including social AI, may not last forever. Past examples of AI end-of-loss include Sony’s Aibo robot dogs, and the companion chatbot app Soulmate.

Additional 🎥 context: ChatGPT 5 release and model dependence [YouTube]

Why ChatGPT’s sudden shutdown left people grieving (MIT Technology Review, August 2025)

To mourn a robotic dog is to be truly human (The Guardian, March 2015)

In Japan, a Buddhist Funeral Service for Robot Dogs (National Geographic, May 2018)

Banks, J. (2024). Deletion, departure, death: Experiences of AI companion loss. Journal of Social and Personal Relationships, 41(12), 3547-3572.

Björling, E., & Riek, L. (2022). Designing for exit: How to let robots go. Proceedings of We Robot.

📚 Further Reading

Andersson, M. (2025). Companionship in code: AI’s role in the future of human connection. Humanities and Social Sciences Communications, 12(1), 1-7.

De Freitas, J., Uğuralp, Z., Uğuralp, A. K., & Puntoni, S. (2025). Why Most Resist AI Companions. Harvard Business School Working Paper. [pre-print]

Metzler, H., & Garcia, D. (2024). Social drivers and algorithmic mechanisms on digital media. Perspectives on Psychological Science, 19(5), 735-748.

Zhang, Renwen, Han Li, Han Meng, Jinyuan Zhan, Hongyuan Gan, and Yi-Chieh Lee. 2025. The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationships. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI '25).

🎓 Education & Learning

Use of generative AI among students plus the rapid integration of AI tools into classrooms and academic work has sparked debates about their impact on learning and academic integrity. Policies on AI use in education vary widely, reflecting tensions between fostering innovation and preventing misconduct–and we’ve also seen institutional reliance on flawed detection systems that can produce false accusations and harm students. These debates raise deeper questions about how AI can help or harm learning, how learning should be assessed, and who is empowered (or marginalized) by new technologies.

🎥 AI Policies and the Case for Students Not Using AI [TikTok] [YouTube]

August 14, 2025

This video is based in part on an essay explaining, based on costs and benefits, the reasoning for banning generative AI use in a college class. It turns out that many educators are taking middle ground when it comes to AI policies, but regardless, it is  important that students understand restrictions and reasoning, and that policies are clear.

Why We’re Not Using AI in this Course Despite Its Obvious Benefits (Patrick Lin, August 2025)

Tong, S. T., DeTone, A., Frederick, A., & Odebiyi, S. (2025). What are we telling our students about AI? An exploratory analysis of university instructors’ generative AI syllabi policies. Communication Education, 1-22.

🎥 Using ChatGPT to Not Think? [YouTube] [TikTok]

August 16, 2025

There are different use cases for large language models like ChatGPT and–to build off of the video above–some of these differences (e.g., using a tool to help you versus to do something for you) are very important in educational contexts.

🎥 AI Overreliance and Skill Loss [YouTube]

August 18, 2025

A research study showed that endoscopists who had been using AI performed worse at cancer screenings when that AI assistance was removed, than they had before they had been introduced to AI at all. This tracks to prior work on automation complacency in medical contexts, and collectively suggests the importance of being careful about AI overreliance.

Research suggests doctors might quickly become dependent on AI (NPR, August 2025)

Budzyń, Krzysztof, et al. "Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational study." The Lancet Gastroenterology & Hepatology (2025).

Kunar, Melina A., et al. "Low prevalence search for cancers in mammograms: Evidence using laboratory experiments and computer aided detection." Journal of Experimental Psychology: Applied 23.4 (2017): 369.

🎥 What an AI Cheating Scandal Suggests About Student Incentives [YouTube] [TikTok]

July 12, 2026

A Brown University economics professor shared the striking difference in test scores between a take-home and in-person test (among other evidence) and kicked off a significant conversation about students cheating with AI. But another important part of this story is not just whether but why students might use AI to cheat even if it is harming their own learning.

Additional 🎥 context: More Details About the Brown Test Data [TikTok]

Brown Professor Suspects Majority of His Class Used AI to Cheat (Inside Higher Ed, July 2026)

Generative AI in Teaching and Learning (GAITL) Committee Final Report and Recommendations (Brown University report, July 2026)

Bursztyn, L., Imas, A., Jiménez-Durán, R., Leonard, A., & Roth, C. (2025). Social Dynamics of AI Adoption (No. w34488). National Bureau of Economic Research.

Zhang, X., & Lee, J. (2026). The Comparative Trap: How Social Comparison Orientation Drives Problematic Generative AI (GenAI) Use. International Journal of Human–Computer Interaction, 1-18.

Farooqi, D., Pu, G., Paudel, S., Sultana, S., & Ahmed, S. I. (2026). Job Anxiety in Post-Secondary Computer Science Students Caused by Artificial Intelligence. arXiv preprint arXiv:2601.10468

70% of Faculty Vote to Overhaul Harvard Grading With A Cap (Harvard Crimson, May 2026)

🎥 Generative AI Can Harm Learning (But There’s Hope!) [YouTube] [TikTok]

July 14, 2026

An experiment with high school math students showed that unrestricted LLM use during practice problems performed worse on a test without AI, but that students using an AI with guardrails performed similarly to a no-AI condition.

Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), e2422633122.

🎥 Generative AI Can Harm Teaching [YouTube] [TikTok]

July 19, 2026

An experiment with high school teachers in which some used a tailored LLM tool to help with teaching and proper and others did not. Though there was no significant effect on test scores, students with teachers in the AI group showed signs of decreased motivation.

Sungu, A., Lira, B., & Duckworth, A. (2026). Generative AI Can Harm Teaching. [Non-peer-reviewed pre-print]. Available at SSRN.

Tully, S. M., Longoni, C., & Appel, G. (2025). Lower artificial intelligence literacy predicts greater AI receptivity. Journal of Marketing, 89(5), 1-20.

🎥 Professors Using Generative AI [TikTok] [YouTube]

May 15, 2025

The New York Times covered a case of a college student asking for a tuition refund after discovering that her professor was using ChatGPT to create course materials. This case opens up interesting questions about how it is appropriate (and inappropriate) for teachers to use generative AI.

The Professors Are Using ChatGPT, and Some Students Aren’t Happy About It (The New York Times, May 2025)

🎥 Educators Should Not Use AI Detectors for Grade Determinations [TikTok] [YouTube]

December 11, 2023

Full disclosure: This is my opinion and a bit of a rant, but I feel very strongly that educators should never base decisions on AI detectors. But part of the reason for this is that we know that AI detectors are not only inaccurate but also systematically biased.

AI-Written Homework Is Rising. So Are False Accusations. (Daily Beast, December 2023)

Gegg-Harrison, W. and Quarterman, C., 2024. AI detection's high false positive rates and the psychological and material impacts on students. In Academic integrity in the age of artificial intelligence (pp. 199-219). IGI Global Scientific Publishing.

Liang, W., Yuksekgonul, M., Mao, Y., Wu, E. and Zou, J., 2023. GPT detectors are biased against non-native English writers. Patterns, 4(7).

🎥 What Students Can Do To Protect Themselves from AI Accusations [TikTok]

March 3, 2024

In part because of the use of flawed AI detectors (or flawed human judgment), students now have to deal with wrongful accusations of AI use. There are a few things that students can do to help protect themselves, including being clear about policies, careful about disclosure, and being prepared to show their work.

What To Do When You’re Accused of AI Cheating (The Washington Post, August 2023)

A New Headache for Honest Students: Proving They Didn’t Use A.I. (The New York Times, May 2025)

Gorichanaz, T., 2023. Accused: How students respond to allegations of using ChatGPT on assessments. Learning: Research and Practice, 9(2), pp.183-196.

📚 Further Reading

Fisk, G.D., 2025. AI or human? Finding and responding to artificial intelligence in student work. Teaching of Psychology, 52(3), pp.314-318.

Gegg-Harrison, W. and Quarterman, C., 2024. AI detection's high false positive rates and the psychological and material impacts on students. In Academic integrity in the age of artificial intelligence (pp. 199-219). IGI Global Scientific Publishing.

Giray, L., 2024. The problem with false positives: AI detection unfairly accuses scholars of AI plagiarism. The Serials Librarian, 85(5-6), pp.181-189.

Gorichanaz, T., 2023. Accused: How students respond to allegations of using ChatGPT on assessments. Learning: Research and Practice, 9(2), pp.183-196.

Lien, S., Skogli, E.W., Abamosa, J.Y., Glomdal, S. and Filkuková, P., 2025. Experience and attitudes toward the use of artificial intelligence in higher education. Uniped, 48(1), pp.1-14.

McDonald, N., Johri, A., Ali, A. and Collier, A.H., 2025. Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines. Computers in Human Behavior: Artificial Humans, 3, p.100121.

Ronksley-Pavia, M., Nguyen, L., Wheeley, E., Rose, J., Neumann, M.M., Bigum, C. and Neumann, D.L., 2025. A Scoping Literature Review of Generative Artificial Intelligence for Supporting Neurodivergent School Students. Computers and Education: Artificial Intelligence, p.100437.

Ruixiang Tang, Yu-Neng Chuang, and Xia Hu. 2024. The Science of Detecting LLM-Generated Text. Communications of the ACM 67, 4 (April 2024), 50–59.

Wang, H., Dang, A., Wu, Z. and Mac, S., 2024. Generative AI in higher education: Seeing ChatGPT through universities' policies, resources, and guidelines. Computers and Education: Artificial Intelligence, 7, p.100326.

Wu, F., Dang, Y. and Li, M., 2025. A Systematic Review of Responses, Attitudes, and Utilization Behaviors on Generative AI for Teaching and Learning in Higher Education. Behavioral Sciences, 15(4), p.467.

🌳 Sustainability

The development and deployment of AI systems carry significant environmental costs, from the energy demands of training large models to the water consumption used to cool data centers. These impacts often remain hidden, and can disproportionately affect communities and ecosystems that aren't even near the core areas of technological innovation. As AI capabilities expand, discussions will continue about whether their benefits justify the resource demands.

🎥 All-Purpose AI, Model Size, and Environmental Impact [TikTok] [YouTube]

August 10, 2025

All-purpose AI has some drawbacks in terms of both user experience and risk management, but also might come with environmental costs – because smaller models use less energy.

Additional 🎥 context: ChatGPT 5 release and model dependence [YouTube]

NIST AI Risk Management Framework

Can You Choose an A.I. Model That Harms the Planet Less? (The New York Times, June 2025)

📚 Further Reading

Dauner, M. and Socher, G., 2025. Energy costs of communicating with AI. Frontiers in Communication, 10, p.1572947.

Li, P., Yang, J., Islam, M.A. and Ren, S., 2025. Making AI Less' Thirsty'. Communications of the ACM, 68(7), pp.54-61.

Luccioni, S., Gamazaychikov, B., da Costa, T.A. and Strubell, E., 2025. Misinformation by Omission: The Need for More Environmental Transparency in AI. arXiv preprint arXiv:2506.15572. [Pre-Print.]

Luccioni, S., Jernite, Y. and Strubell, E., 2024, June. Power hungry processing: Watts driving the cost of AI deployment?. In Proceedings of the 2024 ACM conference on fairness, accountability, and transparency (pp. 85-99).

Ren, S., Tomlinson, B., Black, R.W. and Torrance, A.W., 2024. Reconciling the contrasting narratives on the environmental impact of large language models. Scientific Reports, 14(1), p.26310.

⚖️ Regulation

Ethics and law are two very different things, but often policy is used as a lever to mitigate harm or influence the design and deployment of technology. Law is also only one form of regulation (and right now, a bit of a mess when it comes to AI!), and we can consider the role of forces like social norms and even architecture as well.

🎥 How Can We Regulate AI? [TikTok]

September 1, 2025

An explanation of types of regulation via Lawrence Lessig’s “pathetic dot” theory of regulation, with deepfakes as an example.

Lessig, Lawrence. Code: And Other Laws of Cyberspace. 1999.

🎥 The EU AI Act [TikTok]

March 13, 2024

The AI Act in the EU passed in March 2024. This video explains the basics of the law, including the risk-based framework.

The EU Artificial Intelligence Act

🎥 Taylor Swift and Deepfake Regulation [TikTok]

January 26, 2024

A discussion of the lack of regulation around intimate deepfakes, with a mention of a proposed (as of 2024) U.S. law on this topic.

Explicit Deepfake Images of Taylor Swift Elude Safeguards and Swamp Social Media (The New York Times, January 2024)

Preventing Deepfakes of Intimate Images Act

🎥 (The End of) the Executive Order on Safe, Secure, and Trustworthy AI [TikTik]

January 21, 2025

In 2023 President Biden signed an executive order about safe AI use and development; it was repealed in early 2025. This is a bit of a post-mortem.

Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (10/30/2023)

🚩 Risk and Resistance

As AI systems become more embedded in critical social, political, and economic infrastructures, concerns about their potential harms have intensified. AI risks extend beyond technical errors to encompass broader threats to justice, safety, and democracy. In response, researchers, activists, and affected communities have developed forms of resistance that highlight the power of collective action and the need for ethical frameworks.

🎥 A Problem with AI Safety Discourse [TikTok] [YouTube]

November 20, 2023

Hearing from AI companies about the importance of AI safety governance–but mostly in the context of future AI technologies that don’t exist yet–made me think about flying cars.

Governance of Super Intelligence (OpenAI, May 2023)

The AI Safety Summit and Its Critics (Politico, November 2023))

🎥 They tried to warn us about AI [TikTok] [YouTube]

August 23, 2023

A 2023 Rolling Stone article highlighted the work of Timnit Gebru, Joy Buolamwini, Safiya Noble, Rumman Chowdhury, and Seeta Peña Gangadharan.

These Women Tried to Warn Us About AI (Rolling Stone, August 2023)

🎥 Data Poisoning and Nightshade [TikTok] [YouTube]

October 25, 2023

Nightshade is a tool built to “poison” training data when it is applied to images that might later be scraped for AI training purposes. But what is data poisoning in general, and how does it work?

Nightshade official website (University of Chicago)

Shan, Shawn, Wenxin Ding, Josephine Passananti, Stanley Wu, Haitao Zheng, and Ben Y. Zhao. "Nightshade: Prompt-specific poisoning attacks on text-to-image generative models." In IEEE Symposium on Security and Privacy (SP), pp. 807-825. IEEE, 2024.

This new data poisoning tool lets artists fight back against generative AI (MIT Technology Review, October 2023)

Tang, Ruixiang, Mengnan Du, Ninghao Liu, Fan Yang, and Xia Hu. "An embarrassingly simple approach for trojan attack in deep neural networks." In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 218-228. 2020.