Quickstart

Before starting, install FairBench with:

pip install --upgrade fairbench

A typical workflow using the library will help you identify prospective fairness concerns to discuss with stakeholders. You need to eventually decide which of the concerns matter after getting a wide enough picture. Follow the steps below.

1. Prepare test data

Run your system to generate some predictions for test data. Here, we assess biases for a binary classifier, but other types of predictions can be analysed too. Supported data formats include lists, numpy arrays, and pytorch/tensorflow tensors. Import the library and create some demo predictions to play with:

import fairbench as fb
test, y, yhat = fb.tabular.adult()  # test is a Pandas dataframe

2. Set sensitive attribute fork

Pack sensitive attributes found in your test data into a data structure called fork. This can store any number of attributes with any number of values by considering each value as a separate dimension. A fork can be constructed with many patterns, like this one:

sensitive = fb.Fork(fb.categories @ test[8], fb.categories @ test[9])  # analyse the gender and race columns
sensitive = sensitive.intersectional()  # automatically find non-empty intersections

3. Explore fairness reports

Use sensitive attribute forks alongside predictions to generate fairness reports. Next we generate a multireport, which compares all population groups or subgroups pairwise based on some base performance metrics and aggregates all comparisons to one value. The task type (here: binary classification) and corresponding base performance metrics are determined by which arguments are provided.

report = fb.multireport(predictions=yhat, labels=y, sensitive=sensitive)
fb.describe(report)  # or print(report) or fb.visualize(report) or fb.interactive(report)

Explore reports by backtracking their intermediate computations to get a sense of where unfairness comes from. This can be done either programmatically or through an interactive UI that is also launched programmatically via fb.interactive(report).

4. Create fairness model cards

After determining key issues at play, create some stamps of popular fairness definitions and organize these into a fairness model card. that includes caveats and recommendations. The snippet below creates a card like this one. You can omit the arguments for exporting to a file or for immediately showing the modelcards. You can also export to markdown or yaml formats.

stamps = fb.combine(
    fb.stamps.prule(report),
    fb.stamps.accuracy(report),
    fb.stamps.four_fifths(report)
)
fb.modelcards.tohtml(stamps, file="output.html", show=True)