Responsible AI

Advance your knowledge of today’s AI landscape

Earn a certificate of completion from JHU, a recognized leader in education and research. Earn 2 Continuing Education Units (CEUs) upon course completion.

Course description

Johns Hopkins Engineering Lifelong Learning’s online and asynchronous Responsible AI (RAI) course offers you a practical and in-depth exploration of Responsible AI, with a special focus on Generative AI systems. This course stands out for its balanced approach to ethical principles, governance frameworks, and real-world application. You will master core concepts such as fairness, transparency, and accountability, and develop skills to design mitigation strategies for bias, privacy, and security in high-stakes AI deployments. 

Benefit from expert instruction by AI specialists and Johns Hopkins faculty, who bring extensive experience in AI assurance and ethical AI practices. Gain practical experience and expertise in developing strategies for bias mitigation, privacy protection, and security enhancement as you engage with ethical foundations, generative AI technologies, hands-on projects, and case studies to navigate the complexities of AI deployment. 

Become a leader in ethical AI practices, ensuring that AI technologies are developed and used responsibly for the benefit of society. Enroll now to secure your place in this essential program and take the next step in your AI career. 

Who is the program for?

• AI Project Managers
• Data Scientists and AI Engineers
• Policy Advisers and Compliance Officers
• Educators and Trainers teaching responsible AI practices.
• Healthcare, Defense, and HR Industry Professionals

Acquire essential knowledge in Python

Designed by JHU Faculty, the curriculum covers key areas of gaining proficiency in Python, utilizing generative AI.

• Ethical Foundations
• Bias Mitigation
• Accountability Mapping
• Privacy Protection
• Security Enhancement

Built for Everyone

Dynamic Online Learning

Engage in dynamic, interactive learning from anywhere with JHU faculty in this online, asynchronous course.

Dedicated course support

Johns Hopkins University faculty are readily accessible to address your questions in the course, ensuring a personalized learning experience.

Engaging materials

Content and use cases ensure it is current and relevant to today’s civil space exploration.

Hands-on activities

Hands-on activities that ensure you leave with actionable takeaways.

Faculty

Dr. Jane Pinelis currently serves as the chief AI engineer of the Applied Information Sciences Branch at Johns Hopkins University’s Applied Physics Laboratory (JHU/APL). She leads a diverse group of AI scientists and analysts in the development, assurance, and integration of AI capabilities. Her leadership extends to the formulation of cutting-edge assurance-specific products and stringent standards designed to fortify testing of AI-driven systems across the multifaceted landscape of the U.S. Department of Defense (DOD).

Dr. Pinelis holds a BS in statistics, economics, and mathematics, an MA in statistics, and a PhD in statistics, all from the University of Michigan, Ann Arbor. Prior to her current role, she served as the inaugural chief of AI assurance at the Chief Digital and Artificial Intelligence Office (CDAO) and the Joint Artificial Intelligence Center (JAIC) at the DOD, where she oversaw the test and evaluation (T&E) and responsible AI (RAI) directorates. Her career has largely focused on operational T&E, both in support of the service operational testing commands and at the Office of the Secretary of Defense level.

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