---
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)

subgroups = set(group for group_list in a for group in group_list) - set(['Disability'])
metric.compute(target=a,
                 label=b,
                 output=c,
              subgroups = None)
```