Across the CUNY system, faculty, staff, researchers, and students are working through what AI means for teaching, learning, research, and the day-to-day labor of the university. The CUNY AI Lab (CAIL) is a collaborative initiative based at the Graduate Center that builds AI infrastructure, offers professional development opportunities, and creates teaching resources for the largest urban public university system in the country. Our tools, including the CUNY AI Lab Sandbox, are designed in conversation with the people who use them, so that the wider CUNY community can work with AI without giving up control of their data. We do so under deliberate constraints, such as zero data retention, open-weight models where possible, and no opaque pipelines we cannot explain to the people who use them.

In this blog, we will share updates on our initiatives and reflections about the process.

What You Will Find Here

Our reflection posts will address questions such as:

  • How are CUNY faculty helping us shape the tools they use in their teaching and research?
  • What are undergraduate and graduate students experimenting with AI, and how can that work serve the public good?
  • Why did we choose the open-weight models included in our registry and tools?
  • What kind of labor does it take to make a so-called “automated” workflow accountable to the people whose data passes through it?
  • How does a public university negotiate with vendors who promise AI solutions before the problems have been clearly defined?

Who Will Be Writing

CAIL team members and CAIL graduate student fellows will regularly contribute to the blog. We will also invite contributions from CUNY faculty, librarians, staff, and collaborators from the Critical AI Literacy Institute.

If you teach, study, or work at CUNY and want to write a guest post, get in touch.

If you are reading from outside CUNY, much of what we describe will be specific to our experience working in a large public university system, though we hope some of it will resonate with educators, researchers, and students elsewhere.