Environmental Scan: Canadian Postsecondary Education and AI Ethics
This article provides an overview of key findings from an environmental scan conducted by the We Count team in May 2020. The scan relied on information available through online sources to explore how data ethics are being taught, expressed, and implemented within Canada by three stakeholders in the data ecosystem: Postsecondary Education (PSE) Institutions, Data Service Providers, and AI Firms.
The PSE portion of the environmental scan identified 46 AI-related programs across 29 Canadian PSE institutions and aimed to answer the following questions: Is AI ethics and fairness included in program curriculums? Do any programs address minority groups or outliers?
- 30 of 48 identified programs include an ethics course*
- 17 of 48 identified programs require students to enroll in an ethics course
- 11 of 48 identified programs tackle algorithmic bias, fairness, and social issues**
- 3 of 48 identified programs address minority issues
*Including broader ethical topics such as privacy, security, and cybercrime
**Including required and elective courses
Examples of Ethical AI Courses
University of British Columbia
Course: DSCI 541: Privacy, Ethics, & Security
The legal, ethical, and security issues concerning data, including aggregated data. Proactive compliance with rules and, in their absence, principles for the responsible management of sensitive data. Case studies on privacy, human dignity, harm, the public good, legal issues, the role of ethics boards, and consent.
Simon Fraser University
Course: CMPT 3120: Social Implications
An examination of social processes that are being automated and implications for good and evil, that may be entailed in the automation of procedures by which goods and services are allocated. Examination of what are dehumanizing and humanizing parts of systems and how systems can be designed to have a humanizing effect.
Course: AI Ethics & Policy
This course explores the profound implications of AI on business and society. The ethical and policy issues linked with the application of AI in business are covered in-depth, including such issues as overcoming the job displacement due to AI by job creation, ensuring the public good as AI pervades the new economy, and balancing privacy and transparency in AI related endeavors.
University of Toronto
Course: CSC2541H: AI and Ethics — Mathematical Foundations and Algorithms
Machine learning systems are becoming increasingly important in many domains where they are used to make predictions and decisions that often have life-altering consequences. As these systems are becoming ubiquitous, it is important to address issues of privacy, fairness and accountability. Most of the course will focus on algorithmic fairness.
Course: PHIL 5340: Ethics and Societal Implications of Artificial Intelligence
This course is intended for students with professional interest in the social and ethical implications of AI. Topics include theoretical issues (could AI ever have moral rights?), practical issues (algorithmic bias, labor automation, data privacy), and professional issues (tech industry social responsibility).
University of Western Ontario
Course: CS 9622: Nonfunctional Software Requirements — Safety, Accessibility, & Sustainability
As we increasingly create a society where people have to interact with various automated systems, we need to be concerned about whether these systems could cause personal harm. More generally, there is the need to learn to design computer systems that are not implicitly biased in favor of one portion of the population over another.
University of Waterloo
Course: CS 798: Artificial Intelligence Law, Ethics, and Policy
Students must complete a 3-day workshop on Ethics in Data Science & Artificial Intelligence; alternatively, students can opt for CS 798: Artificial Intelligence Law, Ethics, and Policy.
Ontario Tech University
Course: CSCI 4040U: Ethics, Law, and the Social Impacts of Computing
The development of laws and social mechanisms has not kept pace with the rapid development of computing. The impact that computing has on society will be examined in light of the need for professional ethics and appropriate laws and regulatory agencies.
- Most AI education is through computer science or data science specializations at all PSE degree levels, including continuing studies
- The majority of ethical courses that address social implications and fairness are at the graduate level
- AI ethics is not showing as a priority across programs, with only 17 out of 48 requiring ethics training
- Bias and minority concerns are not addressed often, even in programs with ethics courses
We are pleased to share the findings of this scan through the following accessible formats:
- Word: Ethical AI in the Data Ecosystem
- PDF: Ethical AI in the Data Ecosystem
- PowerPoint: Ethical AI in the Data Ecosystem (with audio)
- Video: Ethical AI in the Data Ecosystem
- YouTube video: Ethical AI in the Data Ecosystem
We have also compiled lists of stakeholders identified during the scan for their proactive stance regarding data ethics and ethical AI: