Overview
The Graduate Certificate in Responsible Artificial Intelligence (AI) for AI Practitioners 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.
Graduate Certificate Application Requirements
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 5710: 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
Bioinformatics and Computational Biology
BCB 5460: Computational Skills for Biological Data
BCB 5670: Algorithms in Bioinformatics
Business
ACCT/MIS 5420: Generative AI in Business Decision-Making
Chemical and Biological Engineering
ChE 5450: Analytical & Numerical Methods
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
Human Development and Family Studies
HDFS 5050: Application of Quantitative Research Methods
Mathematics
MATH 5220X: Mathematical Principles of Data Scienc
Psychology
PSYCH 5010: Foundations of Behavioral Research
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)
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
Graduate Certificate Application Requirements
Contact Us
Email the computer science graduate admissions team at csadmissions@iastate.edu for individual support as you navigate the application process.
This certificate is a permanent outcome of 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.