Teach & case
Help a near-peer reason through the idea — when you explain it, you understand it best — then read the real decision it comes from.
Help Nils check the model
In a minute you'll be the teacher. Get ready to teach Nils: accuracy is not fairness.
Equal accuracy doesn't mean fair. A model can be 95% accurate for both groups while approving one group far less often.
What you've shown you can teach
A case · you decide
Nils and the loan model that scores 95%
Nils — a student whose loan-screening model is 95% accurate for both groups but approves them at different rates
Nils built a model that decides who gets a small-business loan. It's 95% accurate for Group A and 95% accurate for Group B — equal accuracy, which sounds fair. But it approves 60% of Group A and only 40% of Group B. So is it fair? That turns out not to be a math question with one answer.
"Fair" isn't one thing. You could ask for equal SELECTION RATES (both groups approved at the same rate), or equal TRUE-POSITIVE rates (equally-qualified applicants approved equally often), or PREDICTIVE PARITY (a given score means the same real-world risk for everyone). Each is a reasonable meaning of fairness.
A well-known impossibility result says that when the base rates differ between groups, you generally CANNOT satisfy all of these at once — improving one fairness measure trades off against another. So "make it fair" isn't a computation the model hands you; it's a choice among competing values that a person has to make and defend.
Nils has to pick which fairness goal the loan model should serve. Which does the team aim for?
Nils's manager says "it's 95% accurate for both groups, so just call it fair and ship it." How should Nils handle that?
Nils's whole point is that "accuracy" and "fairness" are different questions. Which fairness you choose is a human decision with real trade-offs — the honest move is to name it, defend it, and show the numbers, not to let a high accuracy score quietly stand in for a value you never actually chose.
Who made this?
“In reality, every technology reflects decisions made by designers, companies, and communities, and carries the values and power of those choices.”
- Who benefits from this technology?
- Whose needs are ignored?
When two ideas meet: 2 cases with two people
A case · you decide
Faye and Sena: where to set the line
Faye & Sena — a student adjusting a model’s decision threshold, and a friend who speaks for the people it affects
A school model flags students who may need extra reading support. Faye can move its threshold.
A lower threshold flags more students who need help, and also more who don't.
A higher threshold wrongly flags fewer, but misses more students who needed support.
Sena asks what each mistake costs the student it happens to.
Where should Faye set the threshold?
Faye can move the line. Sena makes sure someone asks who is on each side of it.
Who made this?
“In reality, every technology reflects decisions made by designers, companies, and communities, and carries the values and power of those choices.”
- Who benefits from this technology?
- Whose needs are ignored?
A case · you decide
Nils and Lux: explaining without collecting
Nils & Lux — a student who collects as little data as possible, and a friend who wants every decision explained
A homework-help model gives each student a suggestion. Lux wants a log of why it made each one. Nils points out the log would store what every student typed.
Without the log, nobody can check a strange suggestion later. With it, private questions sit on a server.
What should the team do?
Nils guards what is kept. Lux guards what can be explained. The design has to answer to both.
Who made this?
“In reality, every technology reflects decisions made by designers, companies, and communities, and carries the values and power of those choices.”
- Who benefits from this technology?
- Whose needs are ignored?