Calendar

Introduction

The syllabus is a living document and is liable to change! (You will never get more reading than listed here; readings may be swapped for something of an equivalent length or less. There should be no more than ~30 pages of reading per class session, or around 1-1.5 hr reading.) Please always refer to this document (not paper copies) for the most up-to-date readings and guidelines. If you’re in a time crunch — it happens to the best of us — see if you can finish the absolutely essential readings marked with an asterisk (*) and take the time to step back from today’s conversation if you are not sufficiently informed.

Sep 4

Class expectations (no reading, slides)

• ‼️ Sign up for an MIT Google account (required for viewing some of the documents)
• ‼️ Sign up to take collaborative notes
• Finish syllabus quiz (bring to class on Sep 9)

Sep 9

Theories of media (slides)

• A1 assigned
• Marshall McLuhan, The Medium is the Message (selection, read only pp. 1–10, 1964). 
• Video explanation (2 min) *
  • What is McLuhan’s main argument?
  • Give a concrete example from your own experience that illustrates McLuhan’s point.

Section 1) What is media?

Sep 11

Books and print culture (slides)

• Ann Blair, “Introduction” and “Epilogue” in Too Much to Know (14 pp, 2010) *
  • How did people in early modern Europe collect and search for information?
  • What is the history of “information overload?”

Sep 16

Images and visualization (slides)

• Catherine D’Ignazio and Lauren Klein, “On rational, scientific, objective viewpoints from mythical, imaginary, impossible standpoints” in Data Feminism (~30 pp with pictures, 2020) *
  • What is the “view from nowhere” or the “god-trick?”
  • Is objectivity possible?
• In class RAWGraphs and Google Slide deck

Sep 18

News and newsrooms

• Guest lecture Prof. Seth Mnookin
• Felix M. Simon, “Artificial Intelligence in the News,” Tow Center for Digital Journalism (pp. 3-31: executive summary, intro, chapters 1 and 2). *
  • What are some ways that AI has changed the way that people access and consume news?
  • Can AI mitigate the spread of misinformation?

Sep 23

Radio, TV, and the future of the internet

• Assignment 1 due
• Ethan Zuckerman, “The Case for Digital Public Infrastructure” (2020) *
  • What is infrastructure and why is it important?
  • What would happen if digital infrastructure went public?

Section 2) What is infrastructure?

Sep 25

What makes the internet possible? (slides)

• A2 assigned
• Andrew Blum and Carey Baraka, “Sea change,” Rest of World (2022)
• Vox, How Does the Internet Work? - Glad You Asked S1 (2020)
  • Think back to Zuckerman and his arguments about infrastructure. What are the infrastructural components that make the internet possible?
  • Who is (or should be) responsible for maintaining this infrastructure?
  • What are the consequences of those decisions?
• In class Surfacing.in

Sep 30

What is the cloud? (slides)

• Steven Gonzalez Monserrate, “The Cloud is Material,” MIT SERC Case Studies (2022) *
  • What goes into the maintenance of data centers?
  • How does cloud computing (and therefore ML models) affect the environment?

Oct 2

How do you search and find new information? (slides)

• Guest lecture Phoebe Ayers
• Safiya Noble, “The Power of Algorithms,” (2017, 14 pp) 
• Yair Rosenberg, “How to Take Back Control of What You Read on the Internet,” The Atlantic (2023)
  • What is “the algorithm?”
  • How does algorithmic ranking change the way that you search for information?
• 🗳️ Register to vote on MIT TurboVote: It takes five minutes or less to register, and you can use it to request an absentee ballot and/or subscribe to reminders about registration and voting deadlines and locations in relevant elections.

Oct 7

How is the content moderated? (slides)

• Karen Hao and Deepa Seetharaman, “Cleaning up Chat-GPT takes a heavy toll on human workers,” Wall Street Journal (2023)
• Nate Matias, “The civic labor of volunteer moderators online,” Social Media + Society (2019, 11 pp) *
  • Who or what makes the internet “usable?” How?
  • Should the labor of moderation be compensated? In what ways?
• In class Moderator Mayhem
• In class CMS.100 check-in survey

Section 3) Political economy of new media

Oct 9

Monetizing attention (slides)

• Tim Hwang on The Ezra Klein Podcast (2023), “How the $500 billion attention industry really works,” (listen to the ~1 hr podcast or read the transcript) *
• Nick Seaver, “Captivating algorithms: recommender systems as traps” (2018)
  • How many times were you distracted while doing the readings (and what were those distractions)? (You can do a small count sort of like A1, the media diary.)
    • What is the “attention economy?” Are recommender systems really traps?
  • If not ads and the sale of attention, what are some alternative ways of funding the internet?
• In class Terms and Conditions

Oct 14

Indigenous People’s Day – holiday (no class).

Oct 16

Monetizing personal data (slides)

• Assignment 2 due
• Janet Vertesi, “Data Free Disney,” Public Books (2023) *
• Caltrider, Rykov, and MacDonald, “It’s official: Cars are the worst product category we have ever reviewed for privacy.” Privacy Not Included, Mozilla (2023). 
  • Skim the individual reports on the cars
  • Why should we care about privacy?
  • What are some areas of life where you are willing to exchange privacy for digital services?
• In class Privacy Chicken (CW: fatphobia)

Oct 21

The creator economy (slides)

• A3 assigned
• Fortesa Latifi, “What’s the Price of a Childhood Turned into Content?” Cosmopolitan (2024). 
• Christin and Lu, “The influencer pay gap: Platform labor meets racial capitalism” New Media and Society (2023: 23 pp). *
  • What protections should there be, if any, for underage influencers?
  • How does the creator economy work?

Oct 23

Online communities (slides)

• A4 assigned
• Fiesler, Morrison, and Bruckman, “An Archive of Their Own: A Case Study of Feminist HCI and Values in Design” SIGCHI (2016, 11 pp). *
• Raisa Bruner, “How K-Pop Fans Actually Work as a Force for Political Activism in 2020”, TIME Magazine
  • How are values designed into a digital system like AO3?
  • What are some fandoms that you know of or personally participate in and how do they affect the broader political landscape?

Oct 28

Piecework and machine learning (slides)

• Andy Newman, “I Found Work on an Amazon Website. I Made 97 Cents an Hour.” New York Times (2019) *
• Irani and Silberman, “Turkopticon: interrupting worker invisibility in Amazon Mechanical Turk” SIGCHI (2013, 9 pp)
  • What does the “human in the loop” mean when it comes to the creation of machine learning models?
  • What are some actions that AMT workers can take in order to maintain a more fair compensation system?
• In class Teachable Machine

Oct 30

Instructor attending conference (no class).

Nov 4

Branding and the aesthetics of political campaigns (slides)

• Reason TV, Every Political Ad Ever
  • How do the aesthetics of each campaign contribute to their overall message? What is and isn’t effective?
  • What’s the message in the medium?
• In class Politics of memes

Section 4) Research and writing lab

Nov 6

Studying new media: research methods discussion (slides)

• Review one of these past readings and be prepared to discuss the methods used in the paper!
• Ann Blair, Too Much to Know
• D’Ignazio and Klein, Data Feminism
• Ethan Zuckerman, “Case for Digital Public Infrastructure”
• Blum and Baraka, “Sea change”
• Steven Gonzalez Monserrate, “The Cloud is Material”
• Safiya Noble, “The Power of Algorithms”
• Nate Matias, “The civic labor of volunteer moderators online”
• In class Ruha Benjamin, TL Draw

Nov 11

Veterans Day – holiday (no class)

Nov 13

Studying new media: final project proposals and discussion

• Assignment 3 + 4a due
• Link to final project proposal deck

Nov 15

Writing Lab (no reading) (slides)

• Come to class prepared to work on your project proposals and prep for initial writing / research stage

Section 5) Artificial intelligence and beyond

Nov 20

Quantifying bias and fairness (slides)

• Ochigame, “The Long History of Algorithmic Fairness” (2020) 
• Machine Bias from ProPublica + analysis on how they audited the COMPAS recidivism algorithm *
  • Is it possible to create a “fair” algorithm?
  • How does bias or human error percolate through algorithmic systems?
• In class Can you make AI fairer than a judge? Play our courtroom algorithm game

Nov 25

Large language models and automated text (slides)

• Chiang, “Chat GPT is a Blurry JPEG of the Web,” New Yorker (2023) *
• Bender et al, “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜” (2021, 10 pp)
  • How do LLMs work and what are their limitations?

Nov 27

Rethinking tech regulation

• Guest lecture BU-MIT Tech Clinic
• Electronic Frontier Foundation, “Have You Tried Turning It Off and On Again: Rethinking Tech Regulation and Creative Labor”
  • How do you think tech regulation would meaningfully change the way that people use technology? - What types of regulation would be more effective than others?

Dec 2

Research review week: giving and receiving feedback (slides)

• Assignment 4B due
• George Mason University Writing Center, “Tips For Responding to Someone Else’s Writing”

Dec 4

Practitioner perspectives (slides)

• Rakova et al., “Where Responsible AI Meets Reality: Practitioner Perspectives on Enablers for Shifting Organizational Practices,” (2021, 19 pp) 
  • How do you “implement” responsible AI?
  • What are the major barriers (or opportunities) to doing so?
• In class Trust and Safety Tycoon

Dec 9

Final presentations, part 1

• Assignment 4c due
• Submit to final presentation deck

Dec 11

Final presentations, part 2

• In class Subject evaluations