635.629.8VL - AI Assurance

Information Systems Engineering
Spring 2026

Description

Traditional test and evaluation approaches have focused on verifying that systems meet specified requirements or characterizing their effectiveness. These methods typically involve identifying relevant factors and quantifying uncertainty to determine how many samples are needed. However, AI-enabled systems—and other highly complex systems—vastly expand the possible state space and introduce nonlinear behaviors, making it impractical to gather enough real-world data to confidently ensure reliable performance in critical scenarios. This class addresses this challenge by broadening the types of evidence used to build confidence in system performance. Rather than relying solely on conventional testing, AI Assurance constructs structured arguments that link high-level claims about a system’s behavior to diverse forms of supporting evidence. This includes traditional test data as well as alternative sources, all tied together with clear reasoning and explicitly stated assumptions to justify trust in the system. In this seminar course, students will explore the emerging field of AI Assurance by reading and presenting academic papers, developing and presenting original research, and producing a publication-quality final paper as part of a capstone project.

Instructors

Default placeholder image. No profile image found for Julie Obenauer-Motley.

Julie Obenauer-Motley

Default placeholder image. No profile image found for Jane Pinelis.

Jane Pinelis

Course Structure

The course materials are divided into modules which can be accessed by clicking Course Modules on the course menu. Each week, students will read two papers for a particular module and instructors will select two papers to be presented. In some cases, guest speakers will present on special topics. Over the course, the students will build assurance cases for AI systems of instructor or students’ choice, each module representing a different facet of this assurance case.

Most modules run for a period of seven (7) days, exceptions are noted in the Course Outline. You should regularly check the Calendar and Announcements for assignment due dates.

Course Goals

The primary objective of this course is to equip students with fundamental knowledge and appreciation of different concepts in AI Assurance, and to empower them to play a critical role in creating trustworthy AI systems.

Course Learning Outcomes (CLOs)

Textbooks

No textbook - readings as assigned

Student Coursework Requirements

It is expected that each module will take approximately 7–10 hours per week to complete. Here is an approximate breakdown: reading the assigned sections of the texts (approximately 3–4 hours per week) as well as some outside reading, listening to the audio annotated slide presentations (approximately 2–3 hours per week), and writing assignments, presenting, and commenting on the papers in the form of a blog (approximately 2–3 hours per week).

This course will consist of the following basic student requirements:

Preparation and Participation / Discussion (33.3% of Final Grade Calculation)

You are responsible for carefully reading all assigned material and being prepared for discussion, as well as commenting and participating in discussion.

Post your initial response to the discussion questions by the evening before class for that module week. Posting a response to the discussion question is part one of your grade for module discussions (i.e., Timeliness).

You may choose to use LLMs to prepare your initial response.  You are welcome to do so, but must include as an addendum to your posting your prompts and the LLMs responses, or a summary of the interaction if lengthy.

Part two of your grade for module discussion is your in-class interaction (i.e., Critical Thinking). You will discuss the question in small groups, and each group will present answers to the class.  Feel free to agree or disagree with your classmates, but do ensure that discussions are civil and constructive.

Evaluation of preparation and participation is based on contribution to discussions.

Preparation and participation is evaluated by the following grading elements:

Timeliness (50%)

Critical Thinking (50%)

Preparation and participation is graded as follows:

Presentation (33.3% of Final Grade Calculation)

Each week, two students will present one paper each on the module topic, for 20 minutes. Papers can come from instructors’ suggested reading or students can choose additional relevant papers with instructors’ approval.

Presentations are evaluated by the following grading elements:

  1. Content and Organization (50%)
  2. Clarity and Delivery (50%)

Qualitative assignments are graded as follows:

Course Project (33.3% of Final Grade Calculation)

A course project will be assigned several weeks into the course. The next-to-the-last week will be devoted to the course project.

The course project is evaluated by the following grading elements:

  1. Student preparation and participation (as described in Course Project Description) (40%)
  2. Student technical understanding of the course project topic (as related to individual role that the student assumes and described in the Course Project Description) (20%)
  3. Team preparation and participation (as described in Course Project Description) (20%)
  4. Team technical understanding of the course project topic as described in the Course Project Description) (20%)

Course Project is graded as follows:

Grading Policy

Assignments are due according to the dates posted in your Blackboard course site. You may check these due dates in the Course Calendar or the Assignments in the corresponding modules. We will post grades one week after assignment due dates.

We generally do not directly grade spelling and grammar. However, egregious violations of the rules of the English language will be noted without comment. Consistently poor performance in either spelling or grammar is taken as an indication of poor written communication ability that may detract from your grade.

A grade of A indicates achievement of consistent excellence and distinction throughout the course—that is, conspicuous excellence in all aspects of assignments and discussion in every week.

A grade of B indicates work that meets all course requirements on a level appropriate for graduate academic work. These criteria apply to both undergraduates and graduate students taking the course.

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students. This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University is committed to providing welcoming, equitable, and accessible educational experiences for all students. If disability accommodations are needed for this course, students should request accommodations through Student Disability Services (SDS) as early as possible to provide time for effective communication and arrangements.  For further information about this process, please refer to the SDS Website.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically.  For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity.    If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.