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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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