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

Articles

Documentaries

Other Resources

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.

The internet has drastically changed the landscape of intellectual property, copyright, and enforcement.

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?

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.

Thanks to Eli Francis for making the header photo available freely on unsplash.