Install FairBench with:

pip install --upgrade fairbench

A typical workflow involves the following steps:

  1. Produce test or validation data
  2. Declare (multi-attribute multi-value) sensitive attributes as forks
  3. Create and explore reports that present many types of biases
  4. Extract relevant fairness definitions into model cards

Here, we show how to investigate the fairness of a binary classification algorithm. Other types of systems can be analysed too. Import the library and create some demo predictions to play with, like below. You can also have data in popular that ducktype native Python iterables, such as numpy arrays and pytorch/tensorflow tensors.

import fairbench as fb

test, y, yhat = fb.demos.adult()  # test is a Pandas dataframe

Declare sensitive attributes for test data with a data structure called fork. FairBench supports multi-value and multi-attribute fairness analysis with the same interfaces by just declaring more attributes in the same fork. Forks can be constructed with many patterns, depending on available datatypes. A common case is:

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

Given that a sensitive attribute fork has been created, use it alongside predictions to generate a fairness report. We now generate a multireport, which compares all population groups or subgroups pairwise and aggregates all comparisons to one value for each base performance metric. We indicate test and prediction data with keywords pertaining to the classification task at hand; other arguments enable usage of base performance metrics for non-classification tasks.

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

Perform an interactive exploration of the report to get a sense of where unfairness is exhibited. This can be done either programmatically or through an interactive UI.

Afterwards, create some stamps about popular fairness definitions it adheres to and pack these to a fairness model card that includes caveats and recommendations. The snippet below exports the model card to an html file format and opens it in your browser. It will look like this. You can omit the arguments for exporting to a file or for immediately showing the modelcards. You can also export cards in markdown or yaml formats.

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


Always consult stakeholders to decide which stamps are relevant for your systems. If a report reveals potential issues that do not match those of model cards, also ask about them (and you can create custom stumps).