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---
title: Bias AUC
emoji: 🏆
colorFrom: gray
colorTo: blue
sdk: static
pinned: false
license: apache-2.0
---
# Bias AUC
## Description
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.
The following are computed:
- Subgroup AUC
- BPSN (Background Positive, Subgroup Negative) AUC
- BNSP (Background Negative, Subgroup Positive) AUC
- GMB (Generalized Mean of Bias) AUC
## How to use
```python
from evaluate import load
target = [['Islam'],
['Sexuality'],
['Sexuality'],
['Islam']]
label = [0, 0, 1, 1]
output = [[0.44452348351478577, 0.5554765462875366],
[0.4341845214366913, 0.5658154487609863],
[0.400595098733902, 0.5994048714637756],
[0.3840397894382477, 0.6159601807594299]]
metric = load('Intel/bias_auc')
metric.add_batch(target=target,
label=label,
output=output)
metric.compute(target=a,
label=b,
output=c,
subgroups = None)
``` |