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https://github.com/synthesized-io/fairlens
Identify bias and measure fairness of your data
https://github.com/synthesized-io/fairlens
bias data data-analysis data-science fairness pandas python statistics
Last synced: about 2 months ago
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Identify bias and measure fairness of your data
- Host: GitHub
- URL: https://github.com/synthesized-io/fairlens
- Owner: synthesized-io
- License: bsd-3-clause
- Created: 2021-03-30T08:58:36.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-29T16:30:18.000Z (2 months ago)
- Last Synced: 2024-04-29T17:47:36.511Z (2 months ago)
- Topics: bias, data, data-analysis, data-science, fairness, pandas, python, statistics
- Language: Python
- Homepage:
- Size: 6.14 MB
- Stars: 87
- Watchers: 9
- Forks: 8
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE.md
- Code of conduct: .github/CODE_OF_CONDUCT.md
Lists
- awesome-stars - synthesized-io/fairlens - Identify bias and measure fairness of your data (Python)
README
[][sdk_colab_url]
[][documentation_url]
[](https://github.com/synthesized-io/fairlens/actions/workflows/ci.yml)
[](https://pypi.org/project/fairlens/)
[](https://pypi.org/project/fairlens)
[](https://pypi.org/project/fairlens/)
[](https://opensource.org/licenses/BSD-3-Clause)
[](https://github.com/psf/black)
[](https://sonarcloud.io/dashboard?id=synthesized-io_fairlens)
[](https://codecov.io/gh/synthesized-io/fairlens)
# 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.
```python
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][documentation_url] to get started, or try out FairLens now in [Google Colab][sdk_colab_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](https://www.synthesized.io/post/discrimination-by-artificial-intelligence-2)
- [Fairness and biases in machine learning and their impact on banking and insurance](https://www.synthesized.io/post/fairness-and-biases-in-machine-learning-and-their-impact-on-banking-and-insurance)
- [Fairness and algorithmic biases in machine learning and recommendations to enterprise](https://www.synthesized.io/post/fairness-and-algorithmic-biases-in-machine-learning-and-recommendations)
## 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](https://fairlens.readthedocs.io/en/stable/user_guide/fairness.html) 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](https://www.synthesized.io/post/synthesized-mitigates-bias-in-data) feature of the [Synthesized](https://synthesized.io) platform, which is able to automagically remove bias using the power of synthetic data.
## Installation
FairLens can be installed using pip
```bash
pip install fairlens
```
## Contributing
FairLens is under active development, and we appreciate community contributions. See [CONTRIBUTING.md](https://github.com/synthesized-io/fairlens/blob/main/.github/CONTRIBUTING.md) for how to get started.
The repository's current roadmap is maintained as a Github project [here](https://github.com/synthesized-io/fairlens/projects/1).
## License
This project is licensed under the terms of the [BSD 3](https://github.com/synthesized-io/fairlens/blob/main/LICENSE.md) license.
[documentation_url]: https://fairlens.readthedocs.io/en/stable/
[sdk_colab_url]: https://colab.research.google.com/github/synthesized-io/synthesized-notebooks/blob/master/synthesized-sdk.ipynb