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▣ Practical example: administrative support without delegating the
decision
Context: a tool that helps summarize internal training applications.
Task: extract the stated experience and the proposed objective.
The safe approach: hide names and unnecessary attributes, test varied samples, prevent
the tool from ranking people, and review cases of weak language by hand.
Why this design: it separates reading support from the admission decision.
Verification: compare the extraction against the original text and measure errors by
document type.
FIGURE 4-8: FOUR MEASURES OF FAIRNESS, AND WHY THEY COLLIDE
What to notice: Choosing a fairness measure is a decision, not a calculation. Several of these
measures cannot be satisfied together, so the choice must be stated and justified.
Source: Prepared by the author.
● Remember: Section summary
• Key idea: Harmful bias may arise from data, measurement, context, and use, not from
the model alone.
• Practical decision: Test varied cases, measure errors and effects, and do not use
attributes the task does not need.
• Mistake to avoid: Settling for a check of offensive language or of the overall average
performance.
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