Fall 2019 - CMNS 855 G200

Selected Topics in Communication Studies (5)

Historicizing Big Data & AI

Class Number: 10596

Delivery Method: In Person


  • Course Times + Location:

    Sep 3 – Dec 2, 2019: Fri, 5:30–8:20 p.m.



Specialized one-time graduate course offerings on topics related to the current research of school faculty of visiting professors.


Today, we find confident announcements that AI will bring about humanoid robots, the end of work, perhaps the obsolescence of humanity. But such prognoses look rather different when we consider their own history. Technological fantasies are often recycled over decades, leaving unbuilt prototypes and outmoded theories in their wake, sustaining the same old social problems.

From the 20th century legacy of cybernetics to 1970’s debates around artificial intelligence, from early modern systems of calculation and surveillance to today’s smart doorbells and predictive policing systems, we find a long history of moral dilemmas, enduring biases and unresolved social problems. The latest smart machines too often reprise the classic gender roles of the Jetsons (itself a Flintstones of the future), a pattern also repeated in our most contemporary fictions (e.g. Blade Runner 2049).   

This seminar equips students with a richer, more balanced understanding of how we think and talk about data and AI today. It develops situational awareness of relevant contemporary research in history of science and technology, STS, media and communication studies, as well as machine ethics and critical algorithm studies. There is a strong focus on bringing historical and philosophical lessons back to the pressing debates today – around data privacy and surveillance, Big Tech and anti-trust proposals, fake news and hate speech.


This is a seminar course with an emphasis on in-class discussion around the readings, relevant research, current affairs, and moral / ethical dilemmas. The objective is to develop a more historically, culturally, and philosophically informed position to better address ongoing societal issues surrounding big data and artificial intelligence.


  • Attendance & Participation 20%
  • In-Class Debate 20%
  • Final Project 60%


The school expects that the grades awarded in this course will bear some reasonable relation to established university-wide practices with respect to both levels and distribution of grades.  In addition, the School will follow Policy S10.01 with respect to Academic Integrity, and Policies S10.02, S10.03 and S10.04 as regards Student Discipline.  [Note: as of May 1, 2009 the previous T10 series of policies covering Intellectual Honesty (T10.02) and Academic Discipline (T10.03) have been replaced with the new S10 series of policies.]



No required textbooks; all readings will be on syllabus & provided by instructor.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html