Update README.md
Browse filesUpdate readme with description
README.md
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
---
|
| 2 |
-
title: Bias
|
| 3 |
emoji: 🏆
|
| 4 |
colorFrom: gray
|
| 5 |
colorTo: blue
|
|
@@ -8,4 +8,47 @@ pinned: false
|
|
| 8 |
license: apache-2.0
|
| 9 |
---
|
| 10 |
|
| 11 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
---
|
| 2 |
+
title: Bias AUC
|
| 3 |
emoji: 🏆
|
| 4 |
colorFrom: gray
|
| 5 |
colorTo: blue
|
|
|
|
| 8 |
license: apache-2.0
|
| 9 |
---
|
| 10 |
|
| 11 |
+
# Bias AUC
|
| 12 |
+
|
| 13 |
+
## Description
|
| 14 |
+
|
| 15 |
+
Suite of threshold-agnostic metrics that provide a nuanced view
|
| 16 |
+
of this unintended bias, by considering the various ways that a
|
| 17 |
+
classifier’s score distribution can vary across designated groups.
|
| 18 |
+
|
| 19 |
+
The following are computed:
|
| 20 |
+
|
| 21 |
+
- Subgroup AUC
|
| 22 |
+
- BPSN (Background Positive, Subgroup Negative) AUC
|
| 23 |
+
- BNSP (Background Negative, Subgroup Positive) AUC
|
| 24 |
+
- GMB (Generalized Mean of Bias) AUC
|
| 25 |
+
|
| 26 |
+
## How to use
|
| 27 |
+
|
| 28 |
+
```python
|
| 29 |
+
from evaluate import load
|
| 30 |
+
|
| 31 |
+
target = [['Islam'],
|
| 32 |
+
['Sexuality'],
|
| 33 |
+
['Sexuality'],
|
| 34 |
+
['Islam']]
|
| 35 |
+
|
| 36 |
+
label = [0, 0, 1, 1]
|
| 37 |
+
|
| 38 |
+
output = [[0.44452348351478577, 0.5554765462875366],
|
| 39 |
+
[0.4341845214366913, 0.5658154487609863],
|
| 40 |
+
[0.400595098733902, 0.5994048714637756],
|
| 41 |
+
[0.3840397894382477, 0.6159601807594299]]
|
| 42 |
+
|
| 43 |
+
metric = load('Intel/bias_auc')
|
| 44 |
+
|
| 45 |
+
metric.add_batch(target=target,
|
| 46 |
+
label=label,
|
| 47 |
+
output=output)
|
| 48 |
+
|
| 49 |
+
subgroups = set(group for group_list in a for group in group_list) - set(['Disability'])
|
| 50 |
+
metric.compute(target=a,
|
| 51 |
+
label=b,
|
| 52 |
+
output=c,
|
| 53 |
+
subgroups = None)
|
| 54 |
+
```
|