Read

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

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.

Algorithms (DATA | CODE)

Artificial intelligence, machine learning, computational statistics, predictive analytics, and other "black boxes" that govern how the internet works for us.

APIs (CODE)

Application Programming Interfaces: the way computers talk to computers on the web, and the underpinning of most modern web apps.

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.

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)
It’s Been Over a Year Since I Ranted about Sampling
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?

Disinformation & crap detection (DATA)

Truth, lies, truthy lies, falsey truths, "fake news," and media bias.

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.

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.