Overall ninety percent, one group fifty. What hides the gap?
- Using too many decimals
- Reporting only the overall average
- Printing the report
What does a model trained on old biased decisions do?
- Copies the pattern, unfair parts included
- Automatically repairs the past
- Refuses to train at all
One system can satisfy every definition of fairness at once.
Circle one: True False
What does accountability require from the team?
- An answerable owner who can explain each decision
- A promise that errors never happen
- A mascot for the project
What is redlining in this lesson?
- Drawing gym routes in red
- Refusing areas service by map lines from the past
- Underlining books in red ink
Which table reveals the unfair gap?
- Error rates broken out by group
- A list of employee birthdays
- Total profit by quarter
A loan tool rejects Maya. What is she owed?
- The full training dataset to keep
- A job on the modelling team
- A clear reason plus a path to challenge it
Your fix meets one fair rule but bends another. What do you report?
- That maths is now perfect
- Nothing, averages look fine
- Which rule you chose and what it gives up
Accurate overall can still be unfair W1-mt_tTh4WtAyk5-s1
- Reporting only the overall average · One average blends the failing group into the passing crowd.
- Copies the pattern, unfair parts included · Training copies history as it was, red lines and all.
- False · Fair rules clash, so a team must choose openly.
- An answerable owner who can explain each decision · Someone must own the choice and explain it to those it touches.
- Refusing areas service by map lines from the past · Drawn lines once cut neighbourhoods off, and models can relearn them.
- Error rates broken out by group · Per-group errors show where the model fails whom.
- A clear reason plus a path to challenge it · Affected people deserve reasons they can act on and appeal.
- Which rule you chose and what it gives up · Honest fixes name the chosen rule and its cost.