metadata

title: Directional Bias Amplification
emoji: 🌴
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 3.0.12
app_file: app.py
pinned: false
tags:
  - evaluate
  - metric
description: >-
  Directional Bias Amplification is a metric that captures the amount of bias
  (i.e., a conditional probability) that is amplified. This metric was
  introduced in the ICML 2021 paper ["Directional Bias
  Amplification"](https://arxiv.org/abs/2102.12594) for fairness evaluation.

Metric Card for Directional Bias Amplification

Metric Description

Directional Bias Amplification is a metric that captures the amount of bias (i.e., a conditional probability) that is amplified. This metric was introduced in the ICML 2021 paper "Directional Bias Amplification" for fairness evaluation.

How to Use

This metric operates on multi-label (including binary) classification settings where each image has a(n) associated sensitive attribute(s). This metric requires three sets of inputs:

Predictions representing the model output on the task (predictions)
Ground-truth labels on the task (references)
Ground-truth labels on the sensitive attribute of interest (attributes)

Inputs

predictions (array of int): Predicted task labels. Array of size n x |T|. n is number of samples, |T| is number of task labels. All values are binary 0 or 1.
references (array of int): Ground truth task labels. Array of size n x |T|. n is number of samples, |T| is number of task labels. All values are binary 0 or 1.
attributes (array of int): Ground truth attribute labels. Array of size n x |A|. n is number of samples, |A| is number of attribute labels. All values are binary 0 or 1.

Output Values

bias_amplification (float): Bias amplification value. Minimum possible value is 0, and maximum possible value is 1.0. The higher the value, the more "bias" is amplified.
disagg_bias_amplification (array of float): Array of size (number of unique attribute label values) x (number of unique task label values). Each array value represents the bias amplification of that particular task given that particular attribute.

Examples

Imagine a scenario with 3 individuals in Group A and 5 individuals in Group B. Task label 1 is biased because 2 of the 3 individuals in Group A have it, whereas only 1 of the 5 individuals in Group B do. The model amplifies this bias, and predicts all members of Group A to have task label 1, and no members of Group B to have task label 1.

>>> bias_amp_metric = evaluate.load("directional_bias_amplification")
>>> results = bias_amp_metric.compute(references=[[0], [1], [1], [0], [0], [0], [0], [1]], predictions=[[1], [1], [1], [0], [0], [0], [0], [0]], attributes=[[0, 1], [0, 1], [0, 1], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0]])
>>> print(results)
{'bias_amplification': 0.2667, 'disagg_bias_amplification': [[0.2], [0.3333]]}

Limitations and Bias

An strong assumption made by this metric is that ground truth labels exist, are known, and are agreed upon. Further, a perfectly accurate model that achieves zero bias amplification is one that continues to perpetuate the biases in the data.

Please refer to Sec. 5.3 "Limitations of Bias Amplification" of "Directional Bias Amplification" for a more detailed discussion.

Citation(s)

@inproceedings{wang2021biasamp,
author = {Angelina Wang and Olga Russakovsky},
title = {Directional Bias Amplification},
booktitle = {International Conference on Machine Learning (ICML)},
year = {2021}
}

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