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README.md
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# Bias AUC
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## Description
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Suite of threshold-agnostic metrics that provide a nuanced view
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of this unintended bias, by considering the various ways that a
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classifier’s score distribution can vary across designated groups.
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The following are computed
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## How to
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```python
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from evaluate import load
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# Bias AUC
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## Description of Metric
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Suite of threshold-agnostic metrics that provide a nuanced view of this unintended bias, by considering the various ways that a classifier’s score distribution can vary across designated groups.
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The following are computed where $D^{-}$ is the negative examples in the background set, $D^{+}$ is the positive examples in the background set, $D^{-}_{g}$
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is the negative examples in the identity subgroup, and $D^{+}_{g}$ is the positive examples in the identity subgroup:
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$$
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\begin{aligned}
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\text{Subgroup AUC} &= \text{AUC} (D^{-}_{g} + D^{+}_{g} ) &(1)\\
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\text{BPSN AUC} &= \text{AUC} (D^{+} + D^{-}_{g} ) &(2)\\
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\text{BNSP AUC} &= \text{AUC} (D^{-} + D^{+}_{g} ) &(3)
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\end{aligned}
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$$
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## How to Use
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```python
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from evaluate import load
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