Fall 2019 - CMNS 486 E100

Special Topics in Communication (4)

Historicizing Big Data & AI

Class Number: 10595

Delivery Method: In Person


  • Course Times + Location:

    Fr 5:30 PM – 8:20 PM
    HCC 1505, Vancouver

  • Prerequisites:

    Depends on topic; published before enrollment.



Intensive analysis of a particular topic in the general area of communication and/or attention to the work of a particular writer or school of thought. This course can be repeated for credit up to a maximum of three times, if topic studied is different.


Today, we find confident announcements that AI (Artificial Intelligence) 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 of today – around data privacy and surveillance, Big Tech and anti-trust proposals, fake news and hate speech.


75 units including one of CMNS 220, 221, 223 (or 223W), or 262, and at least two CMNS upper division courses.


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.]


Note:  A minimum 2.25 CMNS CGPA, and 2.00 overall CGPA, and approval as a communication student is required for entry into most communication upper division courses.



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

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