Program Structure
To ensure flexible and meaningful participation across disciplines, the SCAI IGEC includes three complementary pathways that reflect varying levels of depth, focus, and involvement.
Graduate Affiliates
Graduate Affiliates earn the SCAI graduate certificate and become part of a growing intellectual community committed to studying the societal dimensions of AI. Affiliates have access to select SCAI seminars, talks, and networking opportunities, and can showcase relevant work at community events.
Eligibility: Duke graduate and professional students from any school or department.
Certificate Requirements:
- 3 graduate‑level courses (1 in each group)
Optional Opportunities:
- Attendance at the annual SCAI conference
- Participation in the Science Communication Workshop
- Access to seminars and select community events
Graduate Scholars
Graduate Scholars receive one summer of funded support that allow them to focus on interdisciplinary training early in their PhD program. Scholars join a close cohort that participates in workshops, a spring journal club, and a structured summer Work in Progress seminar. Scholars also benefit from priority access to interdisciplinary research mini‑grants, opportunities to engage with visiting speakers, and visibility at the annual SCAI conference. Graduate Scholars will also earn the graduate certificate.
Eligibility: Duke PhD students
Certificate Requirements:
- 3 graduate‑level courses (1 in each group)
Additional Requirements:
- Participation in SCAI events, including the annual conference
- Attendance at biweekly fall workshops and the spring journal club
- Summer Work in Progress seminar
Optional Opportunities:
- Interdisciplinary research grants
- Opportunities for co‑authored research
- Engagement with visiting speakers and collaborators
Graduate Fellows
Graduate Fellows receive a full year of tuition and stipend support to advance dissertation research at the intersection of AI and society. Fellows gain protected time to deepen their scholarly work, contribute leadership to student‑led workshops, and collaborate closely with other SCAI participants. Fellows are also actively involved in program events, including seminars, the annual conference, and cross‑cohort engagement. Graduate Fellows will also earn the graduate certificate.
Eligibility: Duke PhD students typically in year 4 or 5 with an interdisciplinary dissertation related to AI and society
Certificate Requirements:
- 3 graduate‑level courses (1 in each group)
Additional Requirements:
- Proposed dissertation project that foregrounds interdisciplinary research on AI
- Participation in SCAI workshops, events, and the annual conference
- Leadership in student‑led workshops during the fellowship year
Optional Opportunities:
- Collaboration with Scholars and Affiliates on joint research
- Participation in hackathons, poster sessions, and invited speaker events
The information detailing how to apply provides guidance on how the application process differs depending which of the above options you select.
Courses
Students will select one course from each of the three groups: 1) Philosophy and Ethics, 2) Computer Science and Engineering, and 3) Social Science. Together, these groups ensure that every student gains fluency across the ethical, technical, and analytical dimensions of AI.
For course descriptions, visit the Duke Graduate Bulletin.
Classes in this group are designed to provide students with a survey of philosophical issues raised by AI, morality and ethics in AI system development and use, and AI governance.
Students choose one course from the following:
- PHIL 515 Moral Artificial Intelligence
Walter Sinnott-Armstrong (SPRING ONLY) - COMPSCI 590 Moral Artificial Intelligence Research
Brandon Fain (FALL ONLY) - LAW 553 AI Law and Policy
Nita Farahany - BME 590 Ethics in Robotics and Automation
Siobhan Oca - SCISOC 590 AI, Ethics, and Creative Economy Policy
David Hoffman - SCISOC 614 Privacy and Ethical Decision making in our Digital Era
Jolynn Dellinger
Classes in this group are designed to provide students with technical training in the development of trustworthy AI systems. The courses listed below train graduate students in AI security and privacy, fairness, AI alignment, interpretable and explainable machine learning, and machine testing.
Students choose one course from the following:
- COMPSCI 590 Causality Fairness and Explanation
Sudeepa Roy - ECE 590 Robot Learning
Boyuan Chen - ECE 590 AI Security and Privacy
Emily Wenger (FALL ONLY) - AIPI 590 Emerging Trends in Explainable AI
Brinnae Bent (FALL ONLY) - COMPSCI 572 Introduction to Natural Language Processing (Monica Agrawal and Bhuwan Dhingra)
- COMPSCI 527 Computer Vision
Carlo Tomasi - COMPSCI 572
Monica Agrawal/Bhuwan Dhingra - COMPSCI 590 Reinforcement Learning
Ronald Parr - STA 671D Theory and Algorithms for Machine Learning
Cynthia Rudin - ECE 685D Introduction to Deep Learning
Vahid Tarokh
Classes in this group are designed to provide students with rigorous training in the empirical study of human behavior. The courses listed below train graduate students in survey design, causal inference with observational or experimental data, data analysis using non-parametric and Bayesian statistics, and social network analysis.
Students choose one course from the following:
- SOCIOL 590S Society-Centered AI
- SOCIOL 690 Computational Social Science
Chris Bail - SOCIOL 722 Social Statistics 1
Stephen Vaisey - SOCIOL 728 Introduction to Social Networks
James Moody - SOCIOL 690 Data Wrangling and Visualization
Kieran Healy - SOCIOL 722 Social Statistics 1
Stephen Vaisey - PUBPOL 586 Human-Centered Security and Privacy
Pardis Emami Naeini - COMPSCI 653/ECE 653 Human-Centered Computing
Shaundra B Daily - POLSCI 630 Probability and Basic Research
Jon Green, Christopher Johnson - POLSCI 635 Survey Methodology Practicum
Sunshine Hillygus - PSY 766 Data Analysis for Experiments
Maureen Craig - BA 925 Behavioral Decision Theory
Rick Larrick
Extracurricular Opportunities for Course Credit
Bass Connections projects that align significantly with one or more of the three tracks above can be used as course credit. While interdisciplinary research is not a requirement of the program, we anticipate that IGEC faculty and students will propose Bass Connections and Data+ projects that extend the program in new, collaborative ways.
Program Extracurriculars
Workshop Series
SCAI Graduate Scholars and SCAI Graduate Fellows will be required to attend a bi-weekly workshop in their first fall semester and a monthly student-led journal club in the spring semester. The fall semester bi-weekly workshop is designed to bridge the gap between disciplines and help students develop a common language.
Four weeks will be dedicated to each group: 1) Philosophy and Ethics, 2) Computer Science and Engineering, and 3) Social Science, in which fundamental principles of each will be taught and discussed. During the first year of the program, this workshop will be led by faculty directors. In subsequent years, this will be student-led, where each fully-funded student will be required to host two workshops on topics related to their dissertation work.
The monthly spring journal club will align with guest speaker events (described in additional detail below) and will be student-led. We anticipate this club will be pivotal for knowledge sharing and networking within the larger cohort of IGEC scholars and faculty across years.
Summer Work-in-Progress Seminar
SCAI Graduate Scholars and SCAI Graduate Fellows will also be required to attend a summer work-in-progress seminar every other week designed to provide them with interdisciplinary feedback. Because SCAI Graduate Scholars will be in their first three years of graduate work, we assume many will elect to present their master’s paper or equivalent qualifying paper. Because SCAI Graduate Fellows must be in the fourth or fifth year of their PhD program, we assume they will present dissertation research. The summer work-in-progress seminar will be led by a different faculty leader of this IGEC in a rotating fashion. The program will also actively encourage co-authored papers among program participants, via networking sessions among program participants before and after the events described below—where students will also have the opportunity to interact with visiting speakers and faculty.
Ready to take the next step?
Learn how to integrate the SCAI IGEC into your academic path.
How to Engage
