Introduction to Artificial Intelligence: For Faculty

AI output depends entirely on its input, in the form of the prompt it is fed, the dataset used for training and the engineers who create and develop it. This can result in explicit and implicit bias, both unintentional and intentional. 

To “train” the system, generative AI ingests enormous amounts of training data from across the internet. Using the internet as training data means generative AI can replicate the biases, stereotypes, and hate speech found on the web. In addition, 52% of information available on the internet is in English, which means this bias is built into the system through training data. About 70% of people working in AI are male (World Economic Forum, 2023 Global Gender Gap Report) and the majority are white (Georgetown University, The US AI Workforce: Analyzing Current Supply and Growth, January 2024). As a result, there have been numerous cases of algorithmic bias, which is when algorithms make decisions that systematically disadvantage certain groups, in generative AI systems.

While this does not mean that content generated by AI has no value, users should be aware of the possibility of bias influencing AI output.

Text from "Ethics and Privacy - Artificial Intelligence" by University of Texas Libraries under the Creative Commons Attribution NonCommercial 4.0 International License 

Learn more from the video below about what bias in algorithms might look like. 

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.

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.”

Text from "Ethics and Privacy - Artificial Intelligence by University of Texas Libraries under the Creative Commons Attribution NonCommercial 4.0 International License 

There is evidence that AI may contribute to decreasing emissions and finding innovative solutions to climate issues in some sectors. However, there are also serious concerns about the negative environmental impacts that the systems powering AI have. The effects of climate change are not an isolated issue, as they also lead to disproportionate impacts upon socially vulnerable populations. Watch the video below to learn more about the environmental pros and cons of AI use and development.

Where does AI generated content come from?  Because machine learning uses huge data sets, many models use information from the internet in their training. Artists and authors have criticized AI-based tools for using their work for training and generating content without compensation or credit. Watch the video below to learn more about AI and copyright law.

Claiming that AI-generated content is your own work could be considered plagiarism. If you use AI, it is important to properly cite it. 

When using AI, keep in mind the following best practices for privacy:

• Avoid sharing any personal or sensitive information via the AI-powered tools. 
• Do not download Library materials (i.e., articles, ebooks, infographics, psychographics, or other datasets) into AI as it is prohibited.
• Always review the privacy policy of the generative AI tools before utilizing them. Be cautious about policies that permit for the inputted data to be freely distributed to third-party vendors and/or other users. 

Learn more about managing your privacy and data in AI 

Text adapted from "Ethics & Privacy - Artificial Intelligence (Generative) Resources" by Georgetown University under the Creative Commons Attribution NonCommercial 4.0 International License