This unit gives you the analytical toolkit you will use for the rest of the course. You will start with how the world becomes data through classification, measurement, representation, and proxies, and ask why that makes AI a sociotechnical rather than purely technical system.

A career bridge asks what counts as skill and expertise once automation reshapes durable human skills. We then turn to how predictions become decisions through scores, thresholds, and classifications, and who bears the cost when those decisions are wrong.

We close the unit by asking whether predictions can ever be neutral once norms, categories, and power are built into them, and introduce the anticipatory case-study framework you will use in later units.

Fall 2026 dates: Tue, Sep 29 - Tue, Oct 13 (includes Fall Break, Oct 6)

Meeting sequence:

1. How Does the World Become Data, and Why Is AI a Sociotechnical System?
2. What Counts as Skill and Expertise?
3. How Do Predictions Become Decisions?
4. Are Predictions Ever Neutral? Norms, Bias, and Technological Solutionism

By the end of this unit, you should be able to name the sociotechnical toolkit you will apply for the rest of the course: data as produced rather than found, thresholds and error, and the constructedness of norms.