Café Code
Summer Intern Café Code Program
We welcome anyone to use our summer intern Café Code curriculum. It’s a flexible, choose-your-own-adventure curriculum. Here is a quick how-to:
- Choose a weekly meeting time
- Each week’s meeting has three components:
- A short (10-20 min) tutorial - These should be provided at a level that is accessible to everyone, even people with no coding experience. The idea is to open the door, and point to further resources for people who want to dig in. We record and save our video tutorials, so you can use the videos in the “Video Tutorials” list below, or design your own.
- Reading discussion - In advance of the meeting time, assign a reading from the “Articles” list below, or a video from the “Videos and TED talks” list below. These articles and videos cover many of the big picture issues concerning algorithms in science and society. This can be a open discussion, much like in a classroom.
- Open questions - Students are encouraged to bring coding or related questions. These can be answered as a group, or students and mentors can break up into smaller groups to work through questions.
- Use the other resources (code, books, etc.) as you’d like, and have fun.
Video Tutorials
Articles
- What is algorithmic bias?
- AI Is Biased. Here’s How Scientists Are Trying to Fix It
- Teach about climate change with these 24 New York Times graphs
- In Canada, Inuit Communities Are Shaping Research Priorities
- Predictive policing articles:
- Can ‘predictive policing’ prevent crime before it happens?
- LAPD Predictive Policing Tool Raises Racial Bias Concerns
- Predictive Policing Explained
- https://www.theverge.com/2018/4/26/17285058/predictive-policing-predpol-pentagon-ai-racial-bias
- Does ocean acidification alter fish behavior? Fraud allegations create a sea of doubt
- Uncovering Big Data Bias in Sustainability Science
- A Manifesto for Algorithms in the Environment
- How an Algorithm Blocked Kidney Transplants to Black Patients
Videos and TED talks
- Should We Sacrifice Our Data Privacy To Fight The Coronavirus?
- The beauty of data visualization (TED talk)
- Dr. Esther: Highlighting underrespresented scientists, researchers, innovators and more
- AI Literacy, or Why Understanding AI Will Help You Every Day (Jordan Harrod)
- The beauty of data visualization (David McCandless)
- The nightmare videos of children’s YouTube — and what’s wrong with the internet today (James Bridle)
- We’re building a dystopia just to make people click on ads (Zeynep Tufekci)
- Visualizing ourselves … with crowd-sourced data (Aaron Koblin)
- What do we do with all this big data? (Susan Etlinger)
- Gender imbalance in computer science
- The developer migration
Code Resources
- Python for Beginners
- R Studio cheat sheets
- Git primer
- Cheat sheet for Unix / Mac
- Cheat sheet for Windows command prompt / DOS
Books
- Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor , Virginia Eubanks
- Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, Cathy O’Neil
- You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place, Janelle Shane
- Invisible Women: Exposing Data Bias in a World Designed for Men, Caroline Criado Perez
- Life in Code: A Personal History of Technology, Ellen Ullman
- Close to the Machine: Technophilia and its Discontents, Ellen Ullman
- “Raw Data” Is an Oxymoron, Lisa Gitelman (ed)
- The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power, Shoshana Zuboff
- Race After Technology: Abolitionist Tools for the New Jim Code, Ruha Benjamin