Generative Artificial Intelligence (GenAI)

There are a number of ethical and social responsibility concerns that are raised by the use of GenAI. Users should be aware of the considerations and risks related to entering information, consider the caution with which the output should be used, recognize the negative externalities, and think about the larger context of technology and culture that connects to GenAI.

From Understanding Generative Artificial Intelligence (AI) at SUNY Empire 
https://sunyempire.edu/ai/ethical-and-social-responsibility-considerations/

This video describes "five common types of algorithmic bias we should pay attention to: data that reflects existing biases, unbalanced classes in training data, data that doesn't capture the right value, data that is amplified by feedback loops, and malicious data. Now bias itself isn't necessarily a terrible thing, our brains often use it to take shortcuts by finding patterns, but bias can become a problem if we don't acknowledge exceptions to patterns or if we allow it to discriminate." (CrashCourse)

CrashCourse. (13 Dec. 2019) Algorithmic Bias and Fairness: Crash Course AI #18
https://youtu.be/gV0_raKR2UQ?si=_ykNS39ATk1sSozZ

When using GenAI, it is important to evaluate the tool and the tool’s output critically. Ask yourself these questions:

• What is the purpose of the tool?
• How is this tool funded? Does the funding impact the credibility of the output?
• What, if any, ethical concerns do you have about this tool? 
• Does the tool asks you to upload existing content such as an image or paper? If so, are there copyright concerns? Is there a way to opt out of including your uploaded content in the training corpus? 
• What is the privacy policy? How will chatbot conversations and personal data be used? 
• What corpus or data was used to train the tool or is the tool accessing? Consider how comprehensive the data set is (for example, does it consider paywalled information like that in library databases and electronic journals?), if it is current enough for your needs, any bias in the data set, and algorithmic bias.
• Is the information the tool creates or presents accurate and credible? Because generative AI generates content as well as or instead of returning search results, it is important to assess the information to determine accuracy and credibility.
• If any evidence is cited, are the citations real or "hallucinations" (made up citations).

Adapted from University of Texas Libraries Artificial Intelligence (AI) guide
https://guides.lib.utexas.edu/AI

From SUNY Empire, AI Toolkit and Guide, https://sunyempire.edu/ai/ai-toolkit-and-guide/