Fairness, Accountability and the Limits of a Model
A model trained on past decisions reproduces the pattern in them, including the parts nobody would defend out loud. Several reasonable definitions of fairness cannot all hold at once, so a team has to choose one, say which, and be able to explain a decision to the person it lands on.
What a learner can do afterwards
- Show how a model can be accurate overall and much worse for one group
- State two fairness criteria that cannot both hold and say what forces the choice
- Say what a person affected by an automated decision is owed
1 · Read
A model trained on past decisions copies their pattern, including the parts nobody would defend. Old lending maps drew red lines around neighbourhoods, and a model can relearn those lines without anyone naming them. That is why you audit errors group by group.
Your model scores ninety percent overall yet fails half the cases from one neighbourhood. Overall accuracy hides the gap, while a per-group table shows it. The team must say which gap it will close first.
Several fair rules cannot all hold at once, so a team must pick one and say which. Accountability means someone stays answerable for the choice and can explain a decision to the person it lands on. An audit then checks the system kept its promise.
When you fix fairness, name the criterion you chose and what it gives up. A person affected by an automated decision is owed a clear reason and a path to challenge it.
Audit by group, choose one fair rule openly, and stay answerable to every person the model touches.
2 · Watch
Take it off screen
Where it sits
8 questions wait behind this lesson, each with its answer explained. Every answer feeds the sky: stars light as they are learned, and dim when it is time to come back.