University of Toronto
Faculty of Information

Sessional Lecturer

Fall Term 2024 (September - December)

INF2404H – Explainability & Fairness for Responsible Machine Learning

Course Description: Machine Learning applications are increasingly utilized to make crucial decisions in many sectors of our economy and society. These include, but are not limited to, healthcare, financial services, public safety, and higher education. Predictions from machine learning systems are incorporated within organizational processes to support evidence-based decision-making. This course examines state of the art techniques and technologies related to explainability and fairness in machine learning applications. These human-centric aspects play a significant role in the design and operation of machine learning applications. Absence of explainability and fairness capabilities in a machine learning application erodes its public legitimacy and undermines its social licence. This reduces its acceptance and adoption in the real-world. Students will use frameworks and techniques for architectural modeling, analysis, and design to understand explainability and fairness in the context of machine learning applications.
INF2404H - Explainability & Fairness for Responsible Machine Learning

Estimate of the course enrolment: 35

Estimate of TA Support: None anticipated. Estimate of 75 hours with enrollment of 36 or greater. Allocation of TA hours, if any, will be based on enrolment numbers.

Class Schedule: TBD. You are required to be located in geographical proximity to the applicable University premises in order to attend and perform your duties on University premises as of the Starting Date.

Sessional dates of appointment: September 1, 2024 – December 31, 2024

Salary: 
Sessional Lecturer I: $10,300
Sessional Lecturer I Long Term: $10,764
Sessional Lecturer II $11,021
Sessional Lecturer II Long Term: $11,227
Sessional Lecturer III: $11,279
Sessional Lecturer III Long Term: $11,485
 

Please note that should rates stipulated in the collective agreement vary from rates stated in this posting, the rates stated in the collective agreement shall prevail.

Qualifications: Preferably candidates will have a 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.

Brief description of duties: Preparing course materials; delivering course content (e.g., seminars, lectures, and labs); developing and administering course assignments, tests & exams; grading; holding regular office hours. 

Application Deadline: June 25, 2024

Application Process: Applicants must submit a CV and a completed CUPE 3902 Unit 3 application form in one pdf file to the attention of:  

Melissa Szopa, Administrative Coordinator, Academic
Faculty of Information, 140 St. George Street 
University of Toronto
sessional.ischool@utoronto.ca

This job is posted in accordance with the CUPE 3902 Unit 3 Collective Agreement. Preference in hiring is given to qualified individuals advanced to the rank of Sessional Lecturer II and Sessional Lecturer III in accordance with Article 14:12.