Join us for this class:
Tuesday, August 11, 2026, noon-1 p.m., Online
Taught by Alyssa Young
Register for Data Transparency and Privacy in Generative AI Tools*
Are you interested in using generative AI tools but are concerned about lack of data transparency and/or privacy? In this class, we will talk about different options for generative AI tools and how your choice of tool affects your privacy and the transparency of how said tool will use your data. You’ll learn to navigate privacy policies, compare data practices, and evaluate user control options. At the end of the class, you’ll be equipped to make informed decisions about which AI tools align with your privacy and transparency values.
Upon completing this class, you should be able to:
- Define Large Language Models (LLMs) in your own words, drawing connections to artificial intelligence and language processing.
- Identify potential benefits and drawbacks of LLMs.
- Explain the concept of data privacy in the context of LLMs, considering data ownership and usage.
- Define data transparency and its significance for informed user decisions regarding LLMs.
- Locate and access the privacy policies of a few generative AI tools and identify specific sections within the privacy policies that address data privacy and data transparency practices.
- Extract key information from the privacy policies regarding data collection, usage, and user control mechanisms used by generative AI tools.
- Compare and contrast the policies in terms of data collection practices, user control options, data privacy, and transparency measures.
- Critically evaluate the responsible development and use of LLMs in the future, considering ethical implications.
Level: Novice
Recording status: This class will not be recorded.
Class Materials: Class materials will be shared with attendees.
If you are unable to attend this scheduled class, consider requesting a customized instruction session for your course, group, or department.
*HSLS classes are open to University of Pittsburgh faculty, staff, and students, as well as UPMC residents and fellows.