Algorithmic Bias

A significant limitation of artificial intelligence, particularly in generative AI, is the potential for embedded bias in the content it produces. Large Language Models (LLMs) like ChatGPT are trained on vast amounts of publicly available internet data, and are designed to predict the most likely sequence of words in response to a prompt. As a result, these models inevitably reflect and reinforce the biases present in their training data, including social, cultural, political, and linguistic prejudices.

Another layer of potential bias stems from the use of Reinforcement Learning with Human Feedback (RLHF) in model training. While RLHF is intended to improve the quality and safety of AI responses, the human annotators providing feedback are themselves influenced by personal and cultural perspectives, which may be inconsistent, non-neutral, or unrepresentative. Consequently, generative AI tools have been documented to produce content that exhibits socio-political bias and, in some cases, output that is sexist, racist, or otherwise offensive (González Barman, Lohse, & de Regt, 2025).

Recommendations for Responsible Use of Generative AI

• Fact-check all AI-generated information, especially when academic accuracy is critical. Always verify the authenticity and source of any citation.

• Critically assess content for potential bias—AI outputs may unintentionally reflect harmful stereotypes or misleading perspectives.

• Avoid asking AI for bibliographies or reference lists, as it may generate fabricated or inaccurate citations.

• Consult the AI tool’s documentation or update notes to understand its knowledge cutoff, data sources, and intended use cases.

• Remember that generative AI is not a search engine. It does not retrieve factual information from a database but constructs responses based on patterns in its training data.

The Social Dilemma – Bonus Clip: The Discrimination Dilemma

How I'm fighting bias in algorithms | Joy Buolamwini

Attribution

• Buolamwini, J. (2023). Unmasking AI : my mission to protect what is human in a world of machines (First edition.). Random House.
• Georgetown University Library
• González Barman, K., Lohse, S., & de Regt, H. W. (2025). Reinforcement Learning from Human Feedback in LLMs: Whose Culture, Whose Values, Whose Perspectives?: Reinforcement Learning from Human Feedback in LLMs: Whose Culture, Whose Values, Whose Perspectives? Philosophy & Technology, 38(2). https://doi.org/10.1007/s13347-025-00861-0
• Jagat Icon. (n.d.). Bias detection [Graphic]. Canva. https://www.canva.com/

Environmental Impact

"Training a single AI model can emit as much carbon as five cars in their lifetimes." Karen Hao

Generative AI tools require a significant amount of computational processing power to function, which is provided by high-performance servers housed in physical data centers located across the country. These centers require massive amounts of electricity to keep tools operational, as well as water to keep the servers cool. Many AI companies have not revealed just how much electricity and water are used by their tools, or how much will be needed in the future. As such, there are significant unanswered questions about the environmental costs of keeping generative AI tools functional. 

Select Resources on AI and the Environment

• Exploring the Intersection of Climate Justice and Computing in the Social, Ethical, and Responsibility for Computing (SERC) Program

• How Can Scientists Use Artificial Intelligence (AI) to Improve Predictions of River Water Quality?

• How much water does AI consume? The public deserves to know

• AI’s Climate Impact Goes beyond Its Emissions

• Energy and Policy Considerations for Deep Learning in NLP

• The Staggering Ecological Impacts of Computation and the Cloud

• AI for Earth: How NASA’s Artificial Intelligence and Open Science Efforts Combat Climate Change

Image sourced on Canva.com

How AI and data centers impact climate change

CBS Mornings

Attribution

• Attribution: Olympic College; University of Texas Libraries

Privacy in the Age of AI

There are ongoing privacy concerns and uncertainties about how AI systems harvest personal data from users. Users may not realize that the system is also harvesting information like the user’s IP address and their activity while using the service. This is an important consideration when using AI in an educational context, as some students may not feel comfortable having their personal information tracked and saved.

Additionally, OpenAI may share aggregated personal information with third parties in order to analyze usage of ChatGPT. While this information is only shared in aggregate after being de-identified (i.e. stripped of data that could identify users), users should be aware that they no longer have control of their personal information after it is provided to a system like ChatGPT.

Select Resources on AI and Privacy

• Protecting Data Privacy as a Baseline for Responsible AI
• Examining Risks in AI Systems
• Safeguarding Date Privacy in the Age of AI Innovation
• AI and Data Privacy: Protecting Information in a New Era

Attribution

• University of Texas Libraries

Is Content Created by Generative AI Tool Copyrightable?

Currently, copyright protection is not granted to works created by Artificial Intelligence. The U.S. Copyright Office has issued guidance that explains the requirement for human authorship to be granted copyright protection and provides information to creators working in tandem with AI tools on how to effectively and correctly registered their works.

US Copyright Office and Artificial Intelligence – "The Copyright Office has launched an initiative to examine the copyright law and policy issues raised by artificial intelligence (AI) technology, including the scope of copyright in works generated using AI tools and the use of copyrighted materials in AI training."

Copyright Registration Guidance – Guidance for registering Works Containing Material Generated by Artificial Intelligence by the U.S. Copyright Office.

Generative AI Copyright Lawsuits 

• Congressional Research Service – Generative Artificial Intelligence and Copyright Law, updated May 11, 2023
• Harvard Business Review – Generative AI Has an Intellectual Property Problem. by Gil Appel, Juliana Neelbauer, and David A. Schweidel, April 07, 2023

Copyright Issues

The input to generative AI

• Should it be considered fair use? This is widely debated.

Argument A. No it's copyright violation

• Copyright law is AI's 2024 battlefield - "Copyright owners have been lining up to take whacks at generative AI like a giant piñata woven out of their works. 2024 is likely to be the year we find out whether there is money inside," James Grimmelmann, professor of digital and information law at Cornell, tells Axios. "Every time a new technology comes out that makes copying or creation easier, there's a struggle over how to apply copyright law to it." 

This will affect not only OpenAI, but Google, Microsoft, and Meta, since they all use similar methods to train their models.
 

Argument B. Yes, it's fair use

• Thoughts from the Association of Research Libraries 
  • Training Generative AI Models on Copyrighted Works Is Fair Use
• Thoughts from Creative Commons
  • Fair Use: Training Generative AI - Stephen Wolfson
  • Better Sharing for Generative AI - Catherine Stilher
• Thoughts from UC Berkeley Library Copyright Office
  • UC Berkeley Library to Copyright Office: Protect fair uses in AI training for research and education
• Thoughts from EFF: Electronic Frontier Foundation
  • AI Art Generators and the Online Image Market - Katharine Trendacosta and Cory Doctorow
  • How We Think About Copyright and AI Art - Kit Walsh

Several corporations have offered to pay legal bills of users of their tools
Adobe, Google,  Microsoft, and Anthropic (for Claude) have offered to pay any legal bills from lawsuits against users of their tools.

The Output of Generative AI

Can you copyright something you made with AI?
Open AI says:
"... you own the output you create with ChatGPT, including the right to reprint, sell, and merchandise – regardless of whether output was generated through a free or paid plan."

The U.S. Copyright Office says:
The term “author" ... excludes non-humans.

But, if you select or arrange AI-generated material in a sufficiently creative way... In these cases, copyright will only protect the human-authored aspects of the work. For an example, see this story of a comic book. The U.S. Copyright Office determined that the selection and arrangement of the images IS copyrightable, but not the images themselves (made with generative AI).

In other countries, different rulings may apply, see:
Chinese Court’s Landmark Ruling: AI Images Can be Copyrighted

Attribution: 

• University of Arizona Libraries, © [2023] The Arizona Board of Regents on behalf of The University of Arizona, licensed under a Creative Commons Attribution 4.0 International License.
• University of Texas Southwestern Medical Center

ChatGPT and Generative AI Are Hits! Can Copyright Law Stop Them?

Bloomberg Law

Transparency

The increasingly common presence of AI in day-to-day life has heightened the need for transparency in its use: people should be aware of when they are interacting with artificial intelligence, who created the AI they're using, and for what purpose.

Advances in generative AI have made transparency a particular concern. Recent versions of software like ChatGPT can create text in response to a prompt that is indistinguishable from human-produced writing. In academia, this creates concerns over academic integrity in assignments, and is leading to a reevaluation of the types of writing assigned to students. In journalism, some online outlets have already begun publishing articles generated by AI. Given the issues with accuracy in generative AI, a lack of transparency in its use in journalism leads to lower confidence that what we're reading is correct.

Resources

• Ethical Principles for Web Machine Learning

  World Wide Web Consortium (W3C) standards for ethical machine learning.

• UNESCO Recommendation on Ethics in Artificial Intelligence

  UN principles and policies related to AI.

• AI Ethics

  IBM article.

• Institute for Ethics in AI

  Oxford institute researching ethics and governance in AI.

• Exposing.ai

  A search engine that determines whether your images have been used in an AI data set.

Attribution

• Duquesne University- Gumberg Library; Willamette University Libraries; Imaged sourced from canva.com.

Labor

"As we move into a detailed analysis of AI’s role in modern society, the focus shifts to how this technology, while heralded as a tool of efficiency and progress, actually reproduces and exacerbates inequalities. This is evident in the labor practices within the tech industry, where AI development often relies on underpaid and undervalued workers from marginalized communities, perpetuating a cycle of exploitation and exclusion."

Nelson Colón Vargas 

AI At What Cost?

AI still needs human intervention to function properly, but this necessary labor is often hidden. For example, ChatGPT uses prompts entered by users to train its models. Since these prompts are also used to train its subscription model, many consider this unpaid labor.

Taylor & Francis recently signed a $10 million deal to provide Microsoft with access to data from approximately 3,000 scholarly journals. Authors in those journals were not consulted or compensated for the use of their articles. Some argue that using scholarly research to train generative AI will result in better AI tools, but authors have expressed concern about how their information will be used, including whether the use by AI tools will negatively impact their citation numbers

In a more extreme case, investigative journalists discovered that OpenAI paid workers in Kenya, Uganda and India only $1-$2 per hour to review data for disturbing, graphic and violent images. In improving their product, the company exposed their underpaid workers to psychologically scarring content. One worker referred to the work as “torture”.

Attribution:

• University of Texas Libraries; Image sourced on Canva.com
• Vargas, N. C. (2024). Exploiting the Margin: How Capitalism Fuels AI at the Expense of Minoritized Groups. https://doi.org/10.48550/arxiv.2403.06332

Deep Fakes

Deepfakes are videos, images or audio that appear very realistic but are fake. Using AI tools, people can create deep fakes that make it seem like someone has done or said something they have not. This guide from MIT goes in-depth about deep fakes and how to spot them.

Spotting Deepfakes

Identifying deepfakes can be challenging without the assistance of emerging technologies. When watching a video that seems suspicious, follow this advice from MIT Media Lab:

1. Pay attention to the face. High-end DeepFake manipulations are almost always facial transformations. 
2. Pay attention to the cheeks and forehead. Does the skin appear too smooth or too wrinkly? Is the agedness of the skin similar to the agedness of the hair and eyes? DeepFakes are often incongruent on some dimensions.
3. Pay attention to the eyes and eyebrows. Do shadows appear in places that you would expect? DeepFakes often fail to fully represent the natural physics of a scene. 
4. Pay attention to the glasses. Is there any glare? Is there too much glare? Does the angle of the glare change when the person moves? Once again, DeepFakes often fail to fully represent the natural physics of lighting.
5. Pay attention to the facial hair or lack thereof. Does this facial hair look real? DeepFakes might add or remove a mustache, sideburns, or beard. But, DeepFakes often fail to make facial hair transformations fully natural.
6. Pay attention to facial moles.  Does the mole look real? 
7. Pay attention to blinking. Does the person blink enough or too much? 
8. Pay attention to the size and color of the lips. Does the size and color match the rest of the person's face?

Try it out! Can you Detect Fakes?

Remember that even if you can't identify whether the video is a deepfake just by watching it, you can use SIFT to find out more information and determine if the video is trustworthy.

Attribution

• University of Texas Libraries. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 Generic License.
• Illinois State University, Milner Library