Identify bias and measure fairness of your data
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 to get started, or try out FairLens now in Google Colab!
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
Some of the main features of Fairlens are:
Measuring Bias - FairLens can be used to measure the extent and significance of biases in datasets using a wide range of statistical distances and metrics.
Sensitive Attribute and Proxy Detection - Data Scientists may be unaware of protected or sensitive attributes in their data, and potentially hidden correlations between these columns and other non-protected columns in their data. FairLens can quickly identify sensitive columns and flag hidden correlations and the non-sensitive proxies.
Visualization Tools - FairLens has a range of tools that be used to generate meaningful and descriptive diagrams of different distributions in the dataset before delving further in to quantify them. For instance, FairLens can be used to visualize the distribution of a target with respect to different sensitive demographics, or a correlation heatmap.
Fairness Scorer - The fairness scorer is a simple tool which data scientists can use to get started with FairLens. It is designed to just take in a dataset and a target variable and to automatically generate a report highlighting hidden biases, correlations, and containing various diagrams.
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.
FairLens can be installed using pip
pip install fairlens
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.
This project is licensed under the terms of the BSD 3 license.
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