Core readings
These are readings that get at the core of Data, Code, and/or Ethics, as we will engage them during Digital Pedagogy Lab. These are not required readings — and, in fact, there are likely far more of them than anyone would want to dig into in a single week! — but they are a great place to start finding resources for the topics most relevant to you. As with the hands-on activities and the writing prompts, I expect that everyone will do a small minority of these readings. The list is long and broad to give you the room you need to "snorkel" (read broadly) and "scuba" (dive deeply) wherever you see fit.
Each reading is labeled with one or more categories: Data, Code, and/or Ethics. This will help you strike the right balance between the three, whatever the best balance is for you. (I.e., if you are trying to focus equally on each, or if there is one particular area you are keen to really "level up" on.)
Books
- Garrett Grolemund and Hadley Wickham, R for Data Science DATA | CODE
- Kathy O'Neill, Weapons of Math Destruction DATA | ETHICS
- Hans Rosling, Factfulness DATA
- Virginia Eubanks, Automating Inequality ETHICS | DATA
Articles
- Hacking the Attention Economy, danah boyd DATA | ETHICS
- Algorithmic Harms Beyond Facebook and Google: Emergent Challenges of Computational Agency, Zeynep Tufekci DATA | ETHICS
- Teacher Knows If You've Done the E-Reading, David Streitfeld DATA | ETHICS
- Psychographic Profiling and Cambridge Analytica, Kris Shaffer DATA | ETHICS
- What's a Vote Worth? (a.k.a., The Importance of the Quantitative Liberal Arts), Kris Shaffer DATA
- Resources for humans learning about machine learning DATA | CODE
- Introductory Machine Learning Terminology with Food DATA | CODE
Documentaries
- The Great Hack (Netflix) DATA | ETHICS
- Coded Bias (Netflix) DATA | ETHICS
Other Resources
- Domain of One's Own Curriculum: Representation (Gender, Race, Culture, Orientation) DATA | ETHICS
- Public Datasets DATA
- Machine Learning, Kris Shaffer DATA | CODE
Topics for Deeper Engagement
Ad-tech (DATA | ETHICS)
The hidden data collection and tracking behind online advertising.
- Fake news, adtech, and the spread of misinformation
- Adtech and Misinformation: The Middlemen Who Sell to All Sides
- Visualizing the network that connects mainstream and extremist news
- BuzzFeed and Methods for Tracking the Trackers; or This Is Hard, Chapter 9674
Algorithms (DATA | CODE)
Artificial intelligence, machine learning, computational statistics, predictive analytics, and other "black boxes" that govern how the internet works for us.
- Twitter and Tear Gas, Chapter 6
- Data Versus Democracy, Chapter 3
- Google’s “How Search Works”
- How Google’s Algorithm Rules the Web
- Breaking the Black Box: What Facebook Knows About You
- Welcome to the Black Box
- Big Data Algorithms Can Discriminate, and it’s Not Clear What to do About it
- Identity, Power, and Education’s Algorithms
APIs (CODE)
Application Programming Interfaces: the way computers talk to computers on the web, and the underpinning of most modern web apps.
- A journey through API programming ― Part 1: What is an API?
- A journey through API programming ― Part 2: Why APIs?
- A journey through API programming ― Part 3: Retrieving data?
- A journey through API programming ― Part 4: Posting to Medium?
- Trying to Define API Awareness
Attention economy (DATA | ETHICS)
Beyond material goods and the information age, we live in an age of information glut where those who can manage attention ― both theirs and that of others ― will thrive.
- Net Smart, Twitter and Tear Gas
- "Not This One": Social Movements, the Attention Economy, and Microcelebrity Networked Activism
- Social Media and the Decision to Participate in Political Protest: Observations From Tahrir Square
- What Happens to #Ferguson Affects Ferguson: Net Neutrality, Algorithmic Filtering and Ferguson
- Networks of Care and Vulnerability
- The Simon[e] Biles Culture War Traveled Faster Than The News
Censorship (ETHICS)
In the attention economy, censorship is not a denial of information ― which usually backfires ― but a denial of attention.
- Be sure to engage Tufeckci, Twitter and Tear Gas, Chapter 2, which has an important and nuanced take on censorship in the age of the attention economy
- China’s scary lesson to the world: Censoring the Internet works
- Internet Censorship (Amnesty International)
- Internet Censorship and Control (Harvard's Berkman Center)
Copyright, fair use, Creative Commons (ETHICS)
The internet has drastically changed the landscape of intellectual property, copyright, and enforcement.
- Information Doesn't Want to Be Free
- “Building on the Past” (CC video, 2003)
- Aaron Swartz and copyright wars in the Internet age
- Copyright and Fair Use from Stanford Libraries (Specifically, Fair Use section)
- To Cite or to Steal?
- Credit is Always Due
- Copyright Crash Course
Data ownership, privacy, and surveillance (DATA | ETHICS)
Who has access to your data, and what can they do with it?
- Information Doesn't Want to Be Free
- The Web We Need to Give Students
- Do I Own My Domain If You Grade It?
- Looking Up Symptoms Online? These Companies are Tracking You
- Going dark: online privacy and anonymity for normal people
- Reading the Terms of Service for Educational Sites (Or Not)
Disinformation & crap detection (DATA)
Truth, lies, truthy lies, falsey truths, "fake news," and media bias.
- Net Smart, Chapter 2
- Data Versus Democracy
- Spot a Bot: Identifying Automation and Disinformation on Social Media
- #MacronLeaks — how disinformation spreads
- Web Literacy for Student Fact Checkers
- Digital Forensics Research Lab
- Democracy Hacked: A Massive, Pro-Le Pen Disinformation Campaign Hits Twitter, 4chan, and the Mainstream Media
- Truthy Lies and Surreal Truths: A Plea for Critical Digital Literacies
Representation and harassment (ETHICS)
How do algorithms reproduce and magnify bias and stereotypes?
- Twitter and Tear Gas, Chapter 7
- Automating Inequality
- Why I'm Masquerading As A White Bearded Hipster Guy On Twitter (Despite Being a Black Woman)
- The Year I Didn’t Retweet Men
- Why Do My Facebook Friends Look Just Like Me
- Racist, Sexist Tech
Security and safety (DATA | ETHICS)
Protecting ourselves, our data, and our loved ones online.
- Data Versus Democracy, Chapter 4
- Why the Trolls Will Always Win
- GamerGate anger at women all too real for gamemaker"
- Why Women are Attacked by Trolls
- Game of Fear
- What To Expect When You're Expecting (the internet to ruin your life)
- NSA Hacker Chief Explains How to Keep Him Out of Your System
- Edward Snowden Explains How to Reclaim Your Privacy
Supplemental readings
These are resources that are worth diving into if you want to go further or deeper into a topic. They are great for a deep dive during DPL into one or two key issues, or for follow-up after DPL.
- Cory Doctorow, Information Doesn't Want to Be Free: Laws for the Internet Age
- Zeynep Tufekci, Twitter and Tear Gas: The Power and Fragility of Networked Protest
- Kris Shaffer, Data versus Democracy: How Big Data Algorithms Shape Opinions and Alter the Course of History
- Safiya Umoja Noble, Algorithms of Oppression: How Search Engines Reinforce Racism.
Thanks to Eli Francis for making the header photo available freely on unsplash.