AI systems are socio-technical systems.
AI systems are shaped by technical design, institutions, labor, infrastructure, and governance.
What To Notice
When you hear “AI system,” it is easy to imagine just a model, an app, or a piece of code. This pattern asks you to look wider. Every AI system also depends on institutions, labor, funding, hardware, maintenance, supply chains, policy, and stories about what the system is supposed to do.
If you only look at the technical model, you are not looking at the whole system.
Questions To Ask
- Who built, funds, deploys, repairs, and governs this system?
- What infrastructure or supply chains make it possible?
- What human labor is hidden behind the interface?
- Where does accountability live when the system changes or fails?
Why This Matters
Use this pattern as a starting move throughout the course. If a case study starts to sound like it is only about software, pause and ask what else is holding the system together. That habit will help you connect technical mechanisms to institutions, labor, and public consequences much more clearly.
Field Guide
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