Package: responsibly
ResponsiblyToolkit for Auditing and Mitigating Bias and Fairnessof Machine Learning Systems 🔎🤖🧰Responsiblyis developed forpractitionersandresearchersin mind,
but also for learners. Therefore, it is compatible with
data science and machine learning tools of trade in Python,
such as Numpy, Pandas, and especiallyscikit-learn.The primary goal is to be one-shop-stop forauditingbias
and fairness of machine learning systems, and the secondary one
is to mitigate bias and adjust fairness throughalgorithmic interventions.
Besides, there is a particular focus onNLPmodels.Responsiblyconsists of three sub-packages:responsibly.datasetCollection of common benchmark datasets from fairness research.responsibly.fairnessDemographic fairness in binary classification,
including metrics and algorithmic interventions.responsibly.weMetrics and debiasing methods for bias (such as gender and race)
in word embedding.For fairness,Responsibly’s functionality is aligned with the bookFairness and Machine Learning
- Limitations and Opportunitiesby Solon Barocas, Moritz Hardt and Arvind Narayanan.If you would like to ask for a feature or report a bug,
please open anew issueor write us inGitter.RequirementsPython 3.6+InstallationInstall responsibly with pip:$pipinstallresponsiblyor directly from the source code:$gitclonehttps://github.com/ResponsiblyAI/responsibly.git$cdresponsibly$pythonsetup.pyinstallCitationIf you have usedResponsiblyin a scientific publication,
we would appreciate citations to the following:@Misc{,
  author = {Shlomi Hod},
  title =  {{Responsibly}: Toolkit for Auditing and Mitigating Bias and Fairness of Machine Learning Systems},
  year =   {2018--},
  url =    "http://docs.responsibly.ai/",
  note =   {[Online; accessed <today>]}
}Revision History0.1.3 (2021/04/02)Fix new pagacke dependenciesSwitch from Travis CI to Github Actions0.1.2 (2020/09/15)Fix Travis CI issues with pipenvFix bugs with word embedding bias0.1.1 (2019/08/04)Fix a dependencies issue withsmart_openChange URLs to https0.1.0 (2019/07/31)Rename the project toresponsiblyfromethicallyWord embedding biasImprove functionality ofBiasWordEmbeddingThreshold fairness interventionsFix bugs with ROCs handlingImprove API and add functionality (plot_thresholds)0.0.5 (2019/06/14)Word embedding biasFix bug in computing WEATComputing and plotting factual property
association to projections on a bias direction,
similar to WEFAT0.0.4 (2019/06/03)Word embedding biasUnrestrictedmost_similarUnrestrictedgenerate_analogiesRunning specific experiments withcalc_all_weatPlotting clustering by classification
of biased neutral words0.0.3 (2019/04/10)Fairness in ClassificationThree demographic fairness criteriaIndependenceSeparationSufficiencyEqualized odds post-processing algorithmic interventionsComplete two notebook demos (FICO and COMPAS)Word embedding biasMeasuring bias with WEAT methodDocumentation improvementsFixing security issues with dependencies0.0.2 (2018/09/01)Word embedding biasGenerating analogies along the bias directionStandard evaluations of word embedding (word pairs and analogies)Plotting indirect biasScatter plot of bias direction projections between two word embeddingImproved verbose mode0.0.1 (2018/08/17)Gender debiasing for word embedding based on Bolukbasi et al.