Name the outcome.
Be specific about what a system is being asked to achieve.
Field notes · 001
Make the future legible.
A hands-on space for thinking about intelligent systems: what they optimise, who they affect, and what we should ask next.
Choose a lens ↓A systems view / 01
Human goalWhat matters?
→SystemWhat is optimised?
→OutcomeWho experiences it?
A guided exploration
Same system.
Different lens.
Choose a context to see how the questions change. This is a static learning prototype; it does not connect to an AI model.
01 / Choose a context
Where might an intelligent system shape a decision?
Context / Learning support
A system suggests what to study next.
Its recommendation can save time—but what does it treat as progress, and can the learner choose another route?
01 / GoalWhat does “doing well” mean here?
02 / EvidenceWhich signals might be missing?
03 / AgencyWho can change the recommendation?
ILLUSTRATIVE SCENARIO · NOT A PREDICTION OR SYSTEM EVALUATION
Before scaling
Keep people in view.
Capability is one part of a system. Context, incentives and recourse matter too.
Look beyond the user.
Notice everyone who may be affected by the decision.
Keep a way to respond.
Make review, refusal and correction possible.