Sessional Lecturer, INF2258H - Explainability & Fairness for Responsible Machine Learning

University of Toronto • Saint George, UT

1h • Onsite

About The Position

The University of Toronto Faculty of Information is seeking a Sessional Lecturer for the Fall Term 2026 (September – December) to teach INF2258H - Explainability & Fairness for Responsible Machine Learning. This course examines state-of-the-art techniques and technologies related to explainability and fairness in machine learning applications, including generative AI. These human-centric aspects are crucial for the design and operation of machine learning applications, impacting their public legitimacy, social license, acceptance, and adoption. Students will utilize frameworks and techniques for architectural modeling, analysis, and design to understand these concepts within the context of machine learning applications. This course can fulfill the “Professional Values” Requirement.

Requirements

• Completed, or nearly completed, PhD degree in an area related to the course or a Master’s degree plus extensive professional experience in an area related to the course
• Teaching experience is preferred
• Must be located in geographical proximity to the applicable University premises in order to attend and perform duties on University premises as of the Starting Date.

Responsibilities

• Preparing course materials
• Delivering course content (e.g., seminars, lectures, and labs)
• Developing and administering course assignments, tests & exams
• Grading
• Holding regular office hours

What This Job Offers

• Job Type: Part-time
• Career Level: Mid Level