Temporary Microcredential Online Course Instructor: AI Literacy and Policy Fundamentals: Understanding and Mitigating Algorithmic Bias  

Location: Remote (U.S. based only)
Division: Rabb School of Continuing Studies in collaboration with the Heller School of Social Policy and Management, Brandeis University
Compensation: $2,000

Estimated Time Commitment: Approximately 30 -35 hours (4 hours per week) over the eight week course period, including weekly synchronized sessions, preparation, learner support, assignment review, and coordination with program team.

Brandeis University’s Rabb School of Continuing Studies working in collaboration with the Heller School for Social Policy and Management is seeking an experienced Instructor to teach a non-credit-bearing microcredential course titled, “AI Literacy and Policy Fundamentals: Understanding and Mitigating Algorithmic Bias”.

This course will be delivered in a hybrid online modality, combining a weekly synchronous instruction with asynchronous learning activities. The instructor will facilitate live online sessions, guide learners through course materials, review assignments, respond to learner questions, and support an engaging and accessible learning experience.

The course introduces learners to foundational concepts in artificial intelligence, algorithmic bias, responsible AI, and policy approaches to mitigating harm. It examines how AI systems can reproduce or amplify inequities, how bias affects decision-making processes, and what ethical, technical, and policy strategies can be used to build fairer and more accountable AI systems.

The ideal candidate will bring strong subject-matter expertise, experience teaching adult or graduate-level learners, and the ability to translate complex interdisciplinary content into clear, practical, and engaging instruction.

Responsibilities

The Instructor will be responsible for teaching and facilitating the course in accordance with Brandeis academic expectations and course design standards.

Responsibilities include:

• Facilitate one 90-minute synchronous online session each week.

• Prepare for weekly sessions by reviewing course materials, assignments, learner progress, and discussion topics.

• Deliver clear, engaging instruction on AI literacy, algorithmic bias, responsible AI, AI ethics, and policy fundamentals.

• Support learners in connecting course concepts to real-world applications in public policy, social impact, workforce settings, and organizational decision-making.

• Review and provide feedback on assignments, reflections, applied exercises, or other course assessments.

• Respond to learner questions in a timely and professional manner.

• Monitor asynchronous learner engagement, including discussion boards or LMS-based activities, as applicable.

• Coordinate with Brandeis Online, RJxTP, and/or program staff regarding course delivery, learner concerns, scheduling, and completion requirements.

• Uphold Brandeis University’s academic standards, accessibility expectations, and online learning guidelines.

• Participate in brief onboarding or course planning meetings before the start of the course, as needed.

• Provide light post-course input or feedback to support course improvement and future offerings.

Qualifications

• PhD, master’s degree, or equivalent professional qualifications in artificial intelligence, machine learning, public policy, data science, ethics, social policy, technology policy, or a related field.

• Demonstrated expertise in algorithmic bias, responsible AI, AI ethics, public policy, or technology governance.

• Minimum 2 years of relevant professional, academic, or applied experience.

• Prior teaching, training, facilitation, or instructional experience with adult learners, graduate students, or professional audiences.

• Ability to explain complex technical and policy concepts in accessible and practical ways.

• Strong communication, organization, and learner-support skills.

• Comfort using LMS platforms, video conferencing tools, and online teaching technologies.

• Ability to work independently while coordinating with program and online learning teams.

Preferred Experience

• Experience teaching or facilitating online, hybrid, or asynchronous courses.

• Experience working across interdisciplinary fields such as AI, public policy, social justice, health equity, education, workforce development, or technology governance.

• Familiarity with applied case studies involving algorithmic bias in areas such as healthcare, hiring, education, finance, public benefits, criminal justice, or civic systems.

• Experience designing or supporting learner-centered assignments, applied exercises, simulations, or policy analysis activities.

• Experience working with diverse learners, professional audiences, or community-engaged educational programs.

Additional Details

• Fully remote position.

• U.S.-based applicants only.

• No visa sponsorship available.

• Weekly live session commitment: 90 minutes per week.

• Additional expected time includes preparation, assignment review, learner communication, LMS engagement, and coordination with program staff.

• Estimated total commitment: approximately 30–35 hours over the 8 week session.

• Compensation: $2,000.

Pay Range Disclosure

The University's pay ranges represent a good faith estimate of what Brandeis reasonably expects to pay for a position at the time of posting. The pay offered to a selected candidate during hiring will be based on factors such as (but not limited to) the scope and responsibilities of the position, the candidate's work experience and education/training, internal peer equity, and applicable legal requirements.

Equal Opportunity Statement

Brandeis University is an equal opportunity employer which does not discriminate against any applicant or employee on the basis of race, color, ancestry, religious creed, gender identity and expression, national or ethnic origin, sex, sexual orientation, pregnancy, age, genetic information, disability, caste, military or veteran status or any other category protected by law (also known as membership in a "protected class").