Week 2

Lesson 1 Introduction & Course Logistics

• . Course Syllabus.
  Please read through the syllabus

Lesson 2 Intro to HCI for AI

• Holbrook, Jess (July 9, 2017). Human-Centered Machine Learning. Google Design.
• Li, Fei-Fei (March 7, 2018). How to Make A.I. That’s Good for People. New York Times.
• Richard Harper, Tom Rodden, Yvonne Rogers and Abigail Sellen (2008). Being Human: Human-Computer Interaction in the Year 2020 . Microsoft Research.
  Skim Part 1 (pp. 1-33), Read Parts 2-4 (pp. 34-77)

Lab 1 Configure Your Software Stack

Week 3

Martin Luther King Jr. Day - No Class

Lesson 3 Intro to REST

• Avraham, Shif Ben (Sept. 5, 2017). What is REST — A Simple Explanation for Beginners.
  for your reference
• Feilding, Roy (2000). Roy Fielding's original paper.
  for your reference

Lab 2 Create a Simple REST API

The following readings are recommended for additional context:

• . How to query MongoDB via the MongoEngine library.
• . Deploying a web server to Heroku.
• . flask_restful documentation (convenience library creating REST Endpoints).

Week 4

Lesson 4 Modeling & Resource Description: Part 1

Apologies for posting these readings late. We'll go over them in class.

• Glushko, Robert (2013). Chapter 1: Foundations for Organizing Systems. The Discipline of Organizing (TDO).
• Glushko, Robert (2013). The Concept of an Organizing Principle. The Discipline of Organizing (TDO).
• Various authors (2013). Case Studies: Read 12.5 (Organizing a Kitchen), 12.16 (Dabbawalas), and 12.8 (Weekly Newspaper) . The Discipline of Organizing (TDO).

The following readings are recommended for additional context:

• Volkov (2015). Case Study: Carving up the world at its political joints .

Modeling & Resource Description: Part 2

• Glushko, Robert (2013). Ch 2. Design Decisions in Organizing Systems. The Discipline of Organizing (TDO).
• Various authors. (2013). Read 5.3: The Process of Describing Resources. The Discipline of Organizing (TDO).
• Ngo, Vincent (May, 2019). Case Study: Spotify’s Recommendation Engine.

The following readings are recommended for additional context:

• Cho, Anna. Case Study: Guide Dogs for the Blind.
• Danker, Jason (2015). Case Study: Read 12.12 (Autonomous Cars) . The Discipline of Organizing (TDO).

Lab 3 Project Brainstorm

Week 5

Lesson 6 Intro to HTML, CSS, & the DOM

• James Williamson. Intro to HTML. LinkedIn Learning.
• Christina Truong. Intro to CSS: Fundamentals. LinkedIn Learning.
• Christina Truong. CSS Properties. LinkedIn Learning.
• Course Website: HTML Resources (Skim)
• Course Website: CSS Resources (Skim)

Lesson 7 Intro to JavaScript

• Laurence Svekis. JavaScript Chapters 4-6. Introduction to Web Development (O'Reilly).
  Use as a reference

Lab 4 Building a User Interface

Week 6

Lesson 8 User Research

• . Contextual Inquiries. Interaction Design Foundation.
• . How to Conduct User Interviews. Interaction Design Foundation.

The following readings are recommended for additional context:

• Dell, Nicola. Qualitative Interviews.
  Provides some additional strategies for conducting interviews generally

Lesson 9 Personas & Task Scenarios; Prototyping Techniques

• Rosson, M.B. and Carroll, J.M. (2002). Scenario-Based Design. The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications.
• . Personas – A Simple Introduction. Interaction Design Foundation.
• Goodwin, Kim. Perfecting your personas.
• Babich, Nick (2018). The Magic of Paper Prototyping.

Lab 5 "Paper" Prototyping with Balsamiq

• Nielsen Norman Group. Paper Prototyping.

Week 7

Lesson 10 Usability

• Norman, Don (1988). The Design of Everyday Things, Chapter 1.
• Norman, Don (1988). The Design of Everyday Things, Chapter 2.

Lesson 11 Composition & Visual Design Principles

• Williams, Robin (2015). The Non-Designer's Design Book, Chapter 1.
• Williams, Robin (2015). The Non-Designer's Design Book, Chapter 2.

Lab 6 Visual Design

Week 8

Lesson 12 User Testing & Evaluation

• Nielsen/Norman Group (1994). How to Conduct a Heuristic Evaluation.
• Nielsen/Norman Group (1994). 10 Usability Heuristics for User Interface Design.
• Krug, Steve. Don't Make Me Think, Chapter 9.

Lesson 13 Accessibility & Inclusivity

• Ladner, Richard E. Design for User Empowerment. ACM Interactions.
• Wentz, Brian; Paul T.; Lazar, Jonathan (June, 2011). Retrofitting accessibility: The legal inequality of after-the-fact online access for persons with disabilities in the United States. First Monday.

Lab 7 User Testing 1

Week 9

Lesson 14 Explainable / Interpretable AI

• Zittrain, Jonathan (2020). Lecture 2: With Great Power Comes Great Ignorance: What’s Wrong When Machine Learning Gets It Right. The Berkman Klein Center for Internet & Society.
• Weld, Daniel & Bansal, Gagan (June 2019). The Challenge of Crafting Intelligible Intelligence. Communications of the ACM, Vol. 62 No. 6, Pages 70-79.

The following readings are recommended for additional context:

• Rudin, Cynthia (May, 2019). Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nature Machine Intelligence.
• (2018). The Building Blocks of Interpretability. Google Brain + CMU.

Lesson 15 Recommendation Systems

• Swearingen, Kirsten & Sinha, Rashmi. Beyond algorithms: An HCI perspective on recommender systems.
• Cosley, Dan, et al (2003). Is seeing believing? How recommender system interfaces affect users' opinions. SIGCHI.
• Ekstrand, et al. All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. FAT.

The following readings are recommended for additional context:

• Valdez, André; Ziefle, Martina; & Verbert, Katrien (2016). HCI for Recommender Systems: the Past, the Present and the Future.
• Nguyen, Tien et al. Exploring the Filter Bubble: The Effect of Using Recommender Systems on Content Diversity. GroupLens Research, UMN.
• Dacrema, Maurizio; Cremonesi, Paolo; & Jannach, Dietmar (2019). Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches.
• . Intro to Recommendation Systems. Google.

Lab 8 User Testing 2

Week 10

Lesson 16 AI: Challenges, Business Models, & Ideology

• Maréchal, Nathalie & Roberts Biddle, Ellery (2020). It's Not Just the Content, It's the Business Model: Democracy’s Online Speech Challenge. Open Technology Institute.
• Joler, Vladan and Pasquinelli, Matteo (2020). The Nooscope Manifested: AI as Instrument of Knowledge Extractivism.

The following readings are recommended for additional context:

• Katz, Yarden (2017). Manufacturing an Artificial Intelligence Revolution.

Lesson AI: Principles (Ethics, Rights, & Values)

• Fjeld, Jessica; Achten, Nele; Hilligoss, Hannah; Nagy, Adam; Srikumar, Madhulika (Jan 2020). Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI. Berkman Klein Center Research Publication.

The following readings are recommended for additional context:

• Fjeld et al. (Jan 2020). Principled AI: Graphic.
• (Jan 2020). Q&A: Jessica Fjeld on a New Berkman Klein Study of AI Ethical Principles.

Final Project Presentations