[][sdkcolaburl] [][documentation_url]

FairLens

FairLens is an open source Python library for automatically discovering bias and measuring fairness in data. The package can be used to quickly identify bias, and provides multiple metrics to measure fairness across a range of sensitive and legally protected characteristics such as age, race and sex.

Bias in my data?

It's very simple to quickly start understanding any biases that may be present in your data.

import pandas as pd
import fairlens as fl
Load in the data
df = pd.read_csv("datasets/compas.csv")
Automatically generate a report
fscorer = fl.FairnessScorer(
    df,
    target_attribute="RawScore",
    sensitive_attributes=[
        "Sex",
        "Ethnicity",
        "MaritalStatus"
    ]
)
fscorer.demographic_report()

Sensitive Attributes: ['Ethnicity', 'MaritalStatus', 'Sex']
Group Distance  Proportion  Counts   P-Value African-American, Single, Male    0.249    0.291011    5902 3.62e-251       African-American, Single    0.202    0.369163    7487 1.30e-196                        Married    0.301    0.134313    2724 7.37e-193         African-American, Male    0.201    0.353138    7162 4.03e-188                  Married, Male    0.281    0.108229    2195 9.69e-139               African-American    0.156    0.444899    9023 3.25e-133                       Divorced    0.321    0.063754    1293 7.51e-112             Caucasian, Married    0.351    0.049504    1004 7.73e-106                   Single, Male    0.121    0.582910   11822  3.30e-95            Caucasian, Divorced    0.341    0.037473     760  1.28e-76
Weighted Mean Statistical Distance: 0.14081832462333957

Check out the [documentation][documentationurl] to get started, or try out FairLens now in [Google Colab][sdkcolab_url]!

See some of our previous blog posts for our take on bias and fairness in ML:

• Legal consensus regarding biases and fairness in machine learning in Europe and the US
• Fairness and biases in machine learning and their impact on banking and insurance
• Fairness and algorithmic biases in machine learning and recommendations to enterprise

Core Features

• Bias Measurement - Metrics and tests to measure the extent and significance of bias in data using statistical distances and metrics. See the overview for more details.

• Sensitive Attribute and Proxy Detection - Methods to identify legally protected features, and measure hidden correlations between these features and others.

• Visualization Tools - Tools to visualize the distributions of different types of variables or columns in sensitive sub groups.

• Fairness Assessment - A streamlined way of assessing the fairness of an arbitrary dataset, and generating reports highlighting biases and hidden correlations.

The goal of FairLens is to enable data scientists to gain a deeper understanding of their data, and helps to to ensure fair and ethical use of data in analysis and machine learning tasks. The insights gained from FairLens can be harnessed by the Bias Mitigation feature of the Synthesized platform, which is able to automagically remove bias using the power of synthetic data.

Installation

FairLens can be installed using pip

pip install fairlens

Contributing

FairLens is under active development, and we appreciate community contributions. See CONTRIBUTING.md for how to get started.

The repository's current roadmap is maintained as a Github project here.

License

This project is licensed under the terms of the BSD 3 license.

[documentation_url]: https://fairlens.readthedocs.io/en/stable/ [sdkcolaburl]: https://colab.research.google.com/github/synthesized-io/synthesized-notebooks/blob/master/synthesized-sdk.ipynb