Machine Learning & AI
You’ll learn how ubiquitous machine learning is right now and consider just a few of the issues it raises for the information professions.
Read, view, and listen
- People’s Guide to Artificial Intelligence (pamphlet, 67 min.; it’s OK to skim)
- Edward Ongwese, “AI Scams are the Point” (article, 16 min.)
- Bender et al., “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜” (article, 44 min.)
In-class activities
Further reading
Podcasts:
Some useful recent books
- Matteo Pasquinelli, Eye of the Master: A Social History of Artificial Intelligence (2023)
- Dhaliwal et al., Neural Networks (2024)
- Kate Crawford, Atlas of AI (2021)
- Hilke Schellmann, The Algorithm (2024)
(A few) Organizations looking at bias in AI/ML
- Algorithmic Justice League
- Stop LAPD Spying
- ACM Conference on Fairness, Accountability, and Transparency (FAccT)
- Alliance for Public Interest Technology
- Past and upcoming workshops and events
- AI Now Institute at NYU
- Data & Society
- Oxford Internet Institute
- Algorithm Watch
- Center for Critical Internet Inquiry (C2I2) at UCLA