aequitas 1.0.0
The bias and fairness audit toolkit.
License: MIT
Keywords: fairness bias aequitas

# *Aequitas*: Bias Auditing & Fair ML Toolkit

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[comment]: <> (Add badges for coverage when we have tests, update repo for other types of badges!)


<p align="center">
  <img src="https://raw.githubusercontent.com/dssg/aequitas/master/docs/_images/aequitas_logo.svg" width="450">
</p>


`aequitas` is an open-source bias auditing and Fair ML toolkit for data scientists, machine learning researchers, and policymakers. The objective of this package is to provide an easy-to-use and transparent tool for auditing predictors, as well as experimenting with Fair ML methods in binary classification settings.

<p float="left" align="center">
  <a href="#example-notebooks"><img src="https://raw.githubusercontent.com/dssg/aequitas/master/docs/_images/diagram.svg" width="600"/></a>
</p>

## 📥 Installation

```cmd
pip install aequitas
```

or

```cmd
pip install git+https://github.com/dssg/aequitas.git
```

### 🔍 Quickstart on Bias Auditing

To perform a bias audit, you need a pandas `DataFrame` with the following format:

|     | label | score | sens_attr_1 | sens_attr_2 | ... | sens_attr_N |
|-----|-------|-------|-------------|-------------|-----|-------------|
| 0   | 0     | 0     | A           | F           |     | Y           |
| 1   | 0     | 1     | C           | F           |     | N           |
| 2   | 1     | 1     | B           | T           |     | N           |
| ... |       |       |             |             |     |             |
| N   | 1     | 0     | E           | T           |     | Y           |

where `label` is the target variable for your prediction task and `score` is the model output.
Only one sensitive attribute is required; all must be in `Categorical` format.

```python
from aequitas import Audit

audit = Audit(df)
```

To obtain a summary of the bias audit, run:
```python
# Select the fairness metric of interest for your dataset
audit.summary_plot(["tpr", "fpr", "pprev"])
```
<img src="https://raw.githubusercontent.com/dssg/aequitas/master/docs/_images/summary_chart.svg" width="900">

We can also observe a single metric and sensitive attribute:
```python
audit.disparity_plot(attribute="sens_attr_2", metrics=["fpr"])
```
<img src="https://raw.githubusercontent.com/dssg/aequitas/master/docs/_images/disparity_chart.svg" width="900">

### 🧪 Quickstart on Fair ML Experimenting

To perform an experiment, a dataset is required. It must have a label column, a sensitive attribute column, and features.  

```python
from aequitas.flow import DefaultExperiment

experiment = DefaultExperiment(dataset, label="label", s="sensitive_attribute")
experiment.run()
```
Several aspects of an experiment (*e.g.*, algorithms, number of runs, dataset splitting) can be configured individually.

<img src="https://raw.githubusercontent.com/dssg/aequitas/master/docs/_images/pareto_example.png" width="600">

[comment]: <> (Make default experiment this easy to run)

### 🧠 Quickstart on Method Training

Assuming an `aequitas.flow.Dataset`, it is possible to train methods and use their functionality depending on the type of algorithm (pre-, in-, or post-processing).

For pre-processing methods:
```python
from aequitas.flow.methods.preprocessing import PrevalenceSampling

sampler = PrevalenceSampling()
sampler.fit(dataset.train.X, dataset.train.y, dataset.train.s)
X_sample, y_sample, s_sample = sampler.transform(dataset.train.X, dataset.train.y, dataset.train.s)
```

for in-processing methods:
```python
from aequitas.flow.methods.inprocessing import FairGBM

model = FairGBM()
model.fit(X_sample, y_sample, s_sample)
scores_val = model.predict_proba(dataset.validation.X, dataset.validation.y, dataset.validation.s)
scores_test = model.predict_proba(dataset.test.X, dataset.test.y, dataset.test.s)
```

for post-processing methods:
```python
from aequitas.flow.methods.postprocessing import BalancedGroupThreshold

threshold = BalancedGroupThreshold("top_pct", 0.1, "fpr")
threshold.fit(dataset.validation.X, scores_val, dataset.validation.y, dataset.validation.s)
corrected_scores = threshold.transform(dataset.test.X, scores_test, dataset.test.s)
```

With this sequence, we would sample a dataset, train a FairGBM model, and then adjust the scores to have equal FPR per group (achieving Predictive Equality).

## 📜 Features of the Toolkit
- **Metrics**: Audits based on confusion matrix-based metrics with flexibility to select the more important ones depending on use-case.
- **Plotting options**: The major outcomes of bias auditing and experimenting offer also plots adequate to different user objectives. 
- **Fair ML methods**: Interface and implementation of several Fair ML methods, including pre-, in-, and post-processing methods.
- **Datasets**: Two "families" of datasets included, named [BankAccountFraud](https://arxiv.org/pdf/2211.13358) and [FolkTables](https://arxiv.org/abs/2108.04884).
- **Extensibility**: Adapted to receive user-implemented methods, with intuitive interfaces and method signatures.
- **Reproducibility**: Option to save artifacts of Experiments, from the transformed data to the fitted models and predictions.
- **Modularity**: Fair ML Methods and default datasets can be used individually or integrated in an `Experiment`.
- **Hyperparameter optimization**: Out of the box integration and abstraction of [Optuna](https://github.com/optuna/optuna)'s hyperparameter optimization capabilities for experimentation.

### Fair ML Methods

We support a range of methods designed to address bias and discrimination in different stages of the ML pipeline.

<table>
  <tr>
    <th> Type </th>
    <th> Method </th>
    <th> Description </th>
  </tr>
  <tr>
    <td rowspan="5"> Pre-processing </td>
    <td> <a href="https://github.com/dssg/aequitas/blob/master/src/aequitas/flow/methods/preprocessing/data_repairer.py"> Data Repairer </a> </td>
    <td> Transforms the data distribution so that a given feature distribution is marginally independent of the sensitive attribute, s. </td>
  </tr>
  <tr>
    <td> <a href="https://github.com/dssg/aequitas/blob/master/src/aequitas/flow/methods/preprocessing/label_flipping.py"> Label Flipping </a> </td> 
    <td> Flips the labels of a fraction of the training data according to the Fair Ordering-Based Noise Correction method. </td>
  </tr>
  <tr>
    <td> <a href="https://github.com/dssg/aequitas/blob/master/src/aequitas/flow/methods/preprocessing/prevalence_sample.py"> Prevalence Sampling </a> </td>
    <td> Generates a training sample with controllable balanced prevalence for the groups in dataset, either by undersampling or oversampling. </td>
  </tr>
  <tr>
    <td><a href="https://github.com/dssg/aequitas/blob/master/src/aequitas/flow/methods/preprocessing/unawareness.py">Unawareness</td>
    <td>Removes features that are highly correlated with the sensitive attribute.</td>
  </tr>
  <tr>
    <td><a href="https://github.com/dssg/aequitas/blob/master/src/aequitas/flow/methods/preprocessing/massaging.py">Massaging</td>
    <td>Flips selected labels to reduce prevalence disparity between groups.</td>
  <tr>
    <td rowspan="2"> In-processing </td>
    <td> <a href="https://github.com/dssg/aequitas/blob/master/src/aequitas/flow/methods/inprocessing/fairgbm.py"> FairGBM </a> </td>
    <td> Novel method where a boosting trees algorithm (LightGBM) is subject to pre-defined fairness constraints. </td>
  </tr>
  <tr>
    <td><a href="https://github.com/dssg/aequitas/blob/master/src/aequitas/flow/methods/inprocessing/fairlearn_classifier.py">Fairlearn Classifier</td>
    <td> Models from the Fairlearn reductions package. Possible parameterization for ExponentiatedGradient and GridSearch methods.</td>
  </tr>
  <tr>
    <td rowspan="2">Post-processing</td>
    <td><a href="https://github.com/dssg/aequitas/blob/master/src/aequitas/flow/methods/postprocessing/group_threshold.py">Group Threshold</td>
    <td>Adjusts the threshold per group to obtain a certain fairness criterion (e.g., all groups with 10% FPR)</td>
  </tr>
  <tr>
    <td><a href="https://github.com/dssg/aequitas/blob/master/src/aequitas/flow/methods/postprocessing/balanced_group_threshold.py">Balanced Group Threshold</td>
    <td>Adjusts the threshold per group to obtain a certain fairness criterion, while satisfying a global constraint (e.g., Demographic Parity with a global FPR of 10%)</td>
  </tr>
</table>


### Fairness Metrics

`aequitas` provides the value of confusion matrix metrics for each possible value of the sensitive attribute columns To calculate fairness metrics. The cells of the confusion metrics are:

| Cell               | Symbol  | Description                                                    | 
|--------------------|:-------:|----------------------------------------------------------------|
| **False Positive** | $FP_g$  | The number of entities of the group with $\hat{Y}=1$ and $Y=0$ |
| **False Negative** | $FN_g$  | The number of entities of the group with $\hat{Y}=0$ and $Y=1$ |
| **True Positive**  | $TP_g$  | The number of entities of the group with $\hat{Y}=1$ and $Y=1$ |
| **True Negative**  | $TN_g$  | The number of entities of the group with $\hat{Y}=0$ and $Y=0$ |

From these, we calculate several metrics:

| Metric                        | Formula                                             | Description                                                                               | 
|-------------------------------|:---------------------------------------------------:|-------------------------------------------------------------------------------------------| 
| **Accuracy**                  | $Acc_g = \cfrac{TP_g + TN_g}{\|g\|}$                | The fraction of correctly predicted entities withing the group.                           |
| **True Positive Rate**        | $TPR_g = \cfrac{TP_g}{TP_g + FN_g}$                 | The fraction of true positives within the label positive entities of a group.             |
| **True Negative Rate**        | $TNR_g = \cfrac{TN_g}{TN_g + FP_g}$                 | The fraction of true negatives within the label negative entities of a group.             |
| **False Negative Rate**       | $FNR_g = \cfrac{FN_g}{TP_g + FN_g}$                 | The fraction of false negatives within the label positive entities of a group.            |
| **False Positive Rate**       | $FPR_g = \cfrac{FP_g}{TN_g + FP_g}$                 | The fraction of false positives within the label negative entities of a group.            |
| **Precision**                 | $Precision_g = \cfrac{TP_g}{TP_g + FP_g}$           | The fraction of true positives within the predicted positive entities of a group.         |
| **Negative Predictive Value** | $NPV_g = \cfrac{TN_g}{TN_g + FN_g}$                 | The fraction of true negatives within the predicted negative entities of a group.         | 
| **False Discovery Rate**      | $FDR_g = \cfrac{FP_g}{TP_g + FP_g}$                 | The fraction of false positives within the predicted positive entities of a group.        |
| **False Omission Rate**       | $FOR_g = \cfrac{FN_g}{TN_g + FN_g}$                 | The fraction of false negatives within the predicted negative entities of a group.        |
| **Predicted Positive**        | $PP_g = TP_g + FP_g$                                |  The number of entities within a group where the decision is positive, i.e., $\hat{Y}=1$. |
| **Total Predictive Positive** | $K = \sum PP_{g(a_i)}$                              | The total number of entities predicted positive across groups defined by $A$              | 
| **Predicted Negative**        | $PN_g = TN_g + FN_g$                                | The number of entities within a group where the decision is negative, i.e., $\hat{Y}=0$   | 
| **Predicted Prevalence**      | $Pprev_g=\cfrac{PP_g}{\|g\|}=P(\hat{Y}=1 \| A=a_i)$ | The fraction of entities within a group which were predicted as positive.                 | 
| **Predicted Positive Rate**   | $PPR_g = \cfrac{PP_g}{K} = P(A=A_i \| \hat{Y}=1)$   | The fraction of the entities predicted as positive that belong to a certain group.        | 

These are implemented in the [`Group`](https://github.com/dssg/aequitas/blob/master/src/aequitas/group.py) class. With the [`Bias`](https://github.com/dssg/aequitas/blob/master/src/aequitas/bias.py) class, several fairness metrics can be derived by different combinations of ratios of these metrics.

### 📔Example Notebooks

| Notebook | Description |
|-|-|
| [Audit a Model's Predictions](https://colab.research.google.com/github/dssg/aequitas/blob/notebooks/compas_demo.ipynb) | Check how to do an in-depth bias audit with the COMPAS example notebook. |
| [Correct a Model's Predictions](https://colab.research.google.com/github/dssg/aequitas/blob/notebooks/aequitas_flow_model_audit_and_correct.ipynb) | Create a dataframe to audit a specific model, and correct the predictions with group-specific thresholds in the Model correction notebook. |
| [Train a Model with Fairness Considerations](https://colab.research.google.com/github/dssg/aequitas/blob/notebooks/aequitas_flow_experiment.ipynb) | Experiment with your own dataset or methods and check the results of a Fair ML experiment. |

## Further documentation

You can find the toolkit documentation [here](https://dssg.github.io/aequitas/).

For more examples of the python library and a deep dive into concepts of fairness in ML, see our [Tutorial](https://github.com/dssg/fairness_tutorial) presented on KDD and AAAI. Visit also the [Aequitas project website](http://dsapp.uchicago.edu/aequitas/).

## Citing Aequitas


If you use Aequitas in a scientific publication, we would appreciate citations to the following paper:

Pedro Saleiro, Benedict Kuester, Abby Stevens, Ari Anisfeld, Loren Hinkson, Jesse London, Rayid Ghani, Aequitas: A Bias and Fairness Audit Toolkit,  arXiv preprint arXiv:1811.05577 (2018). ([PDF](https://arxiv.org/pdf/1811.05577.pdf))

```bib
   @article{2018aequitas,
     title={Aequitas: A Bias and Fairness Audit Toolkit},
     author={Saleiro, Pedro and Kuester, Benedict and Stevens, Abby and Anisfeld, Ari and Hinkson, Loren and London, Jesse and Ghani, Rayid}, journal={arXiv preprint arXiv:1811.05577}, year={2018}}
``` 

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