Ethics of AI Learning Cohort

Join us for the GradFUTURES Ethics of AI interdisciplinary learning cohort in fall 2022 and spring 2023. Leveraging the expertise of Princeton’s faculty, staff and alumni, this interdisciplinary cohort will be introduced to three pillars of the field of AI Ethics: technical understanding of AI design; business uses cases and causes for concern; and ethical/social theory.  Through guest speakers, case studies and immersive capstone, you will learn, discuss and investigate the ethical implications of AI applications in diverse fields such as tech, healthcare, social media, etc.

Past participants have written widely-read Op-Eds on the topic, taken fellowship opportunities at policy non-profits in the space, and taken positions in industry (including some of the presenters below). Read more about the series.

Learning Objectives

  • Understand and critically evaluate: 
    • The ethical and social implications of emerging AI technologies
    • Differences between AI models like regressions, neural networks and decision trees, and their implications, and 
    • Different learning models for AI, such as supervised, unsupervised, adversarial, and so on
  • Investigate how AI ethics could become part of your academic research, and discover how your research can meaningfully inform the public debate on AI Ethics, or provide an entryway into various career paths in the public and private sectors;
  • Gain practical insights into the tech industry and technology policy-making
  • Acquire relevant professional skills 

Upcoming Events

Check back soon!

“I've seen the A.I. Ethics cohort transform how graduate students think about their future careers—both in academia, and in the growing space of corporate research on the ethical implications of A.I. Each year it's an impressive inter-disciplinary group, bringing computer scientists, engineers, sociologists, psychologists, legal scholars and others together with ethicists. And we all leave the room with new questions to ask about our own disciplines!” 

Steven Kelts, Lecturer, University Center for Human Values

Speakers and Facilitators

Will Goodrum
Principal Data Scientist, GA-CCRi
Rose Guingrich, GS, PSY
Professional Development Associate 2022-23
Social Impact Fellow
GradFUTURES Learning Cohort Participant
Steven Kelts
Lecturer, University Center for Human Values
Facilitator, Ethics of AI Learning Cohort
Clio Hall Award Recipient
Honchan "Sonny" Kim, GS, POL
GradFUTURES Learning Cohort Participant
Sonali Majumdar
Assistant Dean for Professional Development
Angelina Wang, GS, COS
Ph.D. Student, Department of Computer Science
Ben Zevengbergen
Responsible Innovation Ethics & Policy Advisor, Google
Lynne Guey
"The GradFUTURES AI Ethics Learning Cohort introduced me to machine learning and its wide-ranging social implications. Through the program, I learned to think more rigorously about the application of technology to various real-life scenarios. It’s provided an ethical lens for me to think critically about how our tools shape us. One of my projects as a Responsible AI Institute GradFUTURES Fellow involved assisting the Department of Defense with a project to integrate responsible AI practices into its procurement process. This opportunity gave me the chance to direct my graduate studies towards helping an institution with a relatively under-the-radar but pressing issue."

Lynne Guey, Graduate Student, SPIA

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About GradFUTURES Learning Cohorts


GradFUTURES’ interdisciplinary learning cohorts build community among and between graduate students and reinforce each student’s graduate training while drawing on their content knowledge to inform the cohort’s investigation of the topic. As part of the cohort, students will read and discuss books, articles, and case studies. Learning cohorts typically also include at least one experiential component such as an immersive project, a site visit, conference presentation, or fellowship/internship opportunities. Interdisciplinary discussions, reflection, synthesis, community building, and immersive experiences are integral components of each learning cohort experience.