A data scientist proposes using 'equal accuracy' as the sole fairness metric for a healthcare AI triage system. Why is this approach INSUFFICIENT from a fairness perspective?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because equal overall accuracy can mask highly unequal false negative rates across demographic groups — in healthcare, false negatives mean missed diagnoses, and if one group has a significantly higher false negative rate they experience disproportionate harm even when overall accuracy is equal. A is incorrect — equal accuracy can be computed for any classification task.
Full explanation below image
Full Explanation
B is correct because equal overall accuracy can mask highly unequal false negative rates across demographic groups — in healthcare, false negatives mean missed diagnoses, and if one group has a significantly higher false negative rate they experience disproportionate harm even when overall accuracy is equal. A is incorrect — equal accuracy can be computed for any classification task. C is not a fundamental limitation. D is factually incorrect.