Syllabus (2020)

Week 1: Introduction [Theme: how AI shapes us]

Monday Aug 31: No readings due today.

Introduction to class [slides]

Introduction to classmates [slides]

Sign up as a discussant: Open for dates till Sept 16.

Class committee discussion: google doc. Please feel free to take the discussion elsewhere.

Wednesday Sept 2: Reading reflection due today:

  1. Do artifacts have politics? by Langdon Winner

Reminder: write one paragraph about this article. In your writing, you could reflect if this article’s argument applies to your technology choice in Assignment # 0.

Turn in reflection on this google doc.

Week 2: Visions of the Future [Theme: How AI shapes us]

Monday Sept 7: Labor day, no classes. Nothing is due. I recommend you get started on readings due Wednesday early; together they are long.

Wednesday Sept 9:

  1. Utopia? ARTIFICIAL INTELLIGENCE AND LIFE IN 2030 [Introduction only. It ends at p.11. You do not need to read the entire report]
  2. Dystopia? Anthropological/Artificial Intelligence & the HAI
  3. Myopia? Fairness and Abstraction in Sociotechnical Systems (somewhat dense reading. Please consider it carefully when you write your reflection)

Please post your reflection to Canvas.

In class: We’re building a food recommendation system!

Week 3: Case study: recommender systems [Theme: How AI shapes us, how to shape AI]

Monday Sept 14: Reading reflections due today:

  1. Recommender Systems by Paul Resnick and Hal Varian, March 1997. [Fun fact: Hal Varian also led the early development of Google’s ad-auction mechanisms]
  2. “People who like lattes also like…” Why Do Liberals Drink Lattes? 

Suggestion: In your reflection, consider what are the effects of using collaborative-filtering style algorithms for recommendation.

Sidenote: Lattes are a hot topic. A follow-up discussion is here:  The Real Reason Liberals Drink Lattes [This reading is not required]

Wednesday Sept 16: Reading reflections due today:

  1. The Making of a YouTube Radical on the New York Times. Note: this article mentions rape, violence, and contains offensive racial and other slurs. If you are uncomfortable, you may skip this article.
  2. Recommending What Video to Watch Next: A Multitask Ranking System by many authors at Google. [Google owns Youtube]

Suggestion: In your reflection, you could discuss if Youtube’s new system would have made a difference to the protagonist in the New York Times article.

Mini-project 1 due Sept 16: due Sept 20:  Follow a Youtube trail. Start at a video, repeatedly click the “most recommended” video. Where do you end up after 20 videos?

Details: Open Youtube while logged out of your Google account e.g. in incognito mode. You can start at any video, either from the homepage or by searching for a topic. Watch at least 30 seconds of each video, then click the recommended video (The first video in “Up Next”). Write two or three paragraphs about the trajectory of your videos e.g. was there a common theme, did the videos get more “niche” over time (you can measure this by the number of video views.) Did you see older content over time? What about the ads?

Week 4: Privacy [How AI shapes society, How to Shape AI]

Monday Sept 21. Readings discussed in class today:

  1. Protecting Civil Liberties During a Public Health Crisis 
  2. Big data meets Big Brother as China moves to rate its citizens from Wired Magazine

Wednesday Sept 23: Readings discussed in class today:

  1. The Politics of Privacy Theories: Moving from Norms to Vulnerabilities
  2. (optional) Obscurity: A Better Way to Think About Your Data Than ‘Privacy’ 

Week 5: Power [How AI shapes society]

Monday Sept 28 Readings discussed in class today: Everyday powerlessness:

  1. British Grading Debacle Shows Pitfalls of Automating Government
  2. Dark Patterns after GDPR

Wednesday Sept 30 Readings discussed in class today: What do we do now?

  1. Don’t ask if artificial intelligence is good or fair, ask how it shifts power 
  2. Costanza-Chock (2020) Design Justice, Chapter: Design Practices: “Nothing about Us without Us”

NOT required reading: Epistemic Injustice (you might find Chapter 1 interesting.)

Week 6: Infrastructure [How AI shapes society]

Monday October 5 Readings discussed in class today:

The Infrastructure of Experience and the Experience of Infrastructure: Meaning and Structure in Everyday Encounters with Space

Wednesday October 7 Readings discussed in class today: How infrastructure affects us

  1. Access Denied: Faulty Automated Background Checks Freeze Out Renters 
  2. Shopping for Sharpies in Seattle: Mundane Infrastructures of Transnational Design

Mini project Due Sunday Oct 11: Changed from the original: Write about one particular AI system/algorithm you encounter every day (or at least frequently) and how your experience with the system would differ if you had less privilege or different physical infrastructure (e.g. if you did not speak English, had vision impairment, had fewer hours of electricity etc.) The goal is to sketch how physical infrastructure, identity and privilege interact with AI systems.

Grading for this assignment:

  1. 20 points: choice of algorithm – You get more credit for choosing a more commonly used AI system/algorithm
  2. 40 points: Your analysis of how your physical infrastructure and digital infrastructure interact (e.g. what physical/societal infrastructure does your AI algorithm need? Map apps/optimized routing work better when you have reliable addresses, for instance. )
  3. 40 points: Your analysis of how infrastructure (physical and digital) interact with privilege, identity, or ability.

The expectation is you’ll turn in one page. Please do not write more than 2 pages. (i.e. no more than 1000 words)

Week 7: Jobs, Jobs, Jobs! [How AI shapes Us, How AI Shapes Society]

Monday October 12 Readings discussed in class today:

  1. Rosy: The Jobs That Artificial Intelligence Will Create 
  2. Not so rosy: The Humans Working Behind the AI Curtain 

Guiding questions (based on a suggestion by Frank): As you read these readings, consider:

  • What new power does the infrastructure of AI have in terms of job creation and visibility that traditional organizations lack?
  • The readings suggest one mode of AI employment: as auxiliary to AI itself. Is this the only kind of job that might be created?

Wednesday October 14 Readings discussed in class today:

  1. Hazy: The future of work: Why are there still so many jobs?

Anuprita says “this is a great project:

 Note: Oct 16: Midterm grades only include materials turned in before or on this date.

PART 2: So what should we do?

Week 8: Design methods

Monday October 19 Readings discussed in class today:

  1.  Expanding Modes of Reflection in Design Futuring
  2. Optional reading: no reflection required Designing for Slowness, Anticipation and Re-visitation: A Long Term Field Study of the Photobox

Wednesday October 21 Final project activity. No readings today.

Mini project: NOTE slight change based on feedback. For one day, note down all the things you use your smartphone for (alternatively, any personal computing device.) What “jobs” does this device do for you? What “jobs” do you do for the device? (e.g. labeling, explaining, sustaining etc.) What parts of these jobs replace human jobs? Which of these might be enriched by greater human involvement?


  1. 20 points: Credit for demonstrating you observed a whole day.
  2. 40 points: Description of jobs done by device, jobs you do for device.
  3. 40 points: Description of how does it interact with human jobs? Replacement/enrichment/new human jobs.

The expectation is you’ll turn in one page. Please do not write more than 2 pages. (i.e. no more than 1000 words)

Week 9: Interaction models

Monday Oct 26: Humans-in-the-loop?

  1. Scaling B12 Recommendations with a human-in-the-loop recommender system
  2. Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time 

Wednesday Oct 28: Machines in the loop?

  1. A Case for Backward Compatibility for Human-AI Teams
  2. “Hello AI”: Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making

Week 10 Algorithmic issues -1

Monday November 2:

  1. How to recognize AI snake oil
  2. Can we just wait a minute? [1711.06664] Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer 

Week 10 Algorithmic issues – 2

Mon Nov 9

  1. Matthew Kay, Tara Kola, Jessica R. Hullman, Sean Munson (2016) When (ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems

Wed Nov 11

        Project group work — please have a concrete plan of what you want to accomplish during class time!

Optional reading (no reflection!) The Principles and Limits of Algorithm-in-the-Loop Decision Making 

Wednesday November 4: Project activity; no readings.4min presentations of proposal + 4 min of feedback for each team

Week 11 – Algorithms – Opacity

Monday Nov 16: Readings:

  1. The Illusion of Control: Placebo Effects of Control Settings 

Wednesday Nov 18: Guest lecture by Motahhare Eslami.

Reading: User attitudes towards algorithmic opacity and transparency in online reviewing platforms

Week 12: Organizational issues

Mon: Nov 23


  1. Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI 

Wed: Nov 25 is a holiday!!

Week 13: Organizational issues cont’d

Monday Dec 1: Project work day

Wed Dec 3: Guest lecture by Ken Holstein


  1. Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? 

Week 14: Final stretch!

Dec 7: Paper reading:

“This Place Does What It Was Built For”: Designing Digital Institutions for Participatory Change