Overview
The Graduate Certificate in Responsible AI is a 13-credit program designed for AI practitioners, graduate students, and professionals who want advanced training in building, evaluating, and deploying AI systems responsibly. The certificate focuses on risk quantification and risk mitigation across the AI lifecycle, including issues such as bias, opacity, uncertainty, and trust in AI-driven decisions. It is especially well suited for students who want a focused credential in responsible AI without committing to a full minor, as well as students from engineering, statistics, mathematics, bioinformatics, and other disciplines who want to apply AI responsibly in their own fields. The certificate can be taken online or in person to support industry learners and working professionals.
Certificate Coursework
The certificate requires 13 credits and includes four parts: one foundation course, two responsible AI courses or approved research options, one ethics course, and one elective. The structure is designed to give students both technical depth and flexibility across disciplines. Students with stronger AI backgrounds can choose advanced technical foundations, while students seeking a more applied entry point can begin with the online practical AI course.
1. Foundation course — choose one (3 credits)
- COMS 5720: Principles of Artificial Intelligence
- COMS 5730: Machine Learning
- COMS 5740: Introduction to Machine Learning
- COMS 5705X: Practical Artificial Intelligence and Machine Learning (online)
- EE 5260: Deep Learning Theory and Practice
2. Responsible AI requirement — choose two (6 credits total)
- COMS 5710X: Responsible AI: Risk Management in Data Driven Discovery
- COMS 6710X: Responsible AI: Advanced Topics in Risk Management in Data Driven Discovery
- Creative component or research course in the student’s graduate major, with a topic related to responsible AI, data science, or machine learning
3. Ethics requirement — required (1 credit)
- PHIL 5430X: Ethics of Artificial Intelligence
4. Elective — choose one approved elective
Computer Science
- COMS 5190X: Trustworthy Healthcare Software
- COMS 5130: Foundations and Applications of Program Analysis
- COMS 5150: Software System Safety
- COMS 5160: Artificial Intelligence in Software Engineering
- COMS 5270: High Performance Deep Learning
- COMS 5350: Algorithms for Large Data Sets: Theory and Practice
- COMS 5760: Motion Planning for Robotics and Autonomous Systems
- COMS 5770: Foundations of Robotics and Computer Vision
- COMS 5780: Optimization for Machine Learning
- COMS 5790: Natural Language Processing
- COMS 6650B: Advanced Topics in Software Engineering Empirical
- COMS 6720: Advanced Topics in Artificial Intelligence
- COMS 6730: Advanced Topics in Machine Learning
Statistics
- STAT 5830: Empirical Methods for Computational Science
- STAT 5010: Multivariate Statistical Methods (for Statistics majors)
- STAT 5020: Applied Modern Multivariate Statistical Learning (for Statistics majors)
- STAT 5101: Statistical Methods for Data Analysis (for non-Statistics majors)
Mathematics
- MATH 5220X: Mathematical Principles of Data Science
Chemical and Biological Engineering
- ChE 5450: Analytical & Numerical Methods
Bioinformatics and Computational Biology
- BCB 5460: Computational Skills for Biological Data
- BCB 5670: Algorithms in Bioinformatics
For students from non-CS disciplines and for working professionals, COMS 5705X offers an especially practical entry point through hands-on work in data wrangling, visualization, predictive modeling, deep learning, transfer learning, large language models, and retrieval-augmented generation.
Eligibility
Applicants must hold a bachelor’s degree. At minimum, students should have prior preparation in basic calculus, introductory programming and problem solving, and introductory statistics. Depending on the courses selected, students must also satisfy the prerequisites of those courses. The proposal identifies students from computer science, mathematics, statistics, engineering, bioinformatics, physics, chemistry, linguistics, psychology, economics, and the MS in AI as strong matches for the program.
Cost of the Certificate
Tuition and fees are set by Iowa State University and are subject to change. Please consult the official tuition and fees pages for current rates. If and when the certificate is offered in a distance format, students pursuing a distance certificate as their only program may be eligible for the published distance certificate tuition rate.
Application Process
Applicants should apply through Iowa State’s Graduate College application system. The university’s current graduate admissions process asks applicants to create an Admissions MyAccount, complete the application, submit required documents, and then accept an offer if admitted. On the program side, applicants to this certificate would be reviewed using admission criteria developed by the Certificate Committee, and they should be prepared to demonstrate background in calculus, programming, and statistics, along with any course-specific prerequisites.
Deadlines
Application deadlines will be posted here once finalized.
Contact Us
For program questions, the proposal names Dr. Wallapak Tavanapong as the contact person:
Lauren Maras
Program Specialist Computer Science
lmaras@iastate.edu
For general department questions, the current Computer Science department page lists csdept@iastate.edu and 515-294-3407.
This certificate is partially supported by the National Science Foundation under Grant No. 2152117. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.