Package: biasondemand
Bias on DemandBiasondemand is a Python package that generates synthetic datasets with different types of bias. This package is based on the research paper "Bias on Demand: A Modelling Framework That Generates Synthetic Data With Bias" published at the ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT) 2023.Authors & ContributorsJoachim Baumann, Alessandro Castelnovo, Riccardo Crupi, Nicole Inverardi, Daniele RegoliInstallationTo use biasondemand, run:pip install biasondemandUsageGenerating Synthetic DatasetsTo generate a synthetic dataset with no bias, use the following python script:import biasondemand
biasondemand.generate_dataset(path='my_unbiased_dataset', dim=1000)Alternatively, you can also run it directly from the command line using the following command:bias_on_demand_generate_dataset -p my_unbiased_dataset -dim 1000This will generate a dataset with 1000 rows and save it in the directorydatasets/my_unbiased_dataset/.You can introduce different types of bias into the dataset by specifying command line arguments. For example, to generate a dataset with measurement bias on the label Y (magnitude: 1.5) and historical bias on the feature R (magnitude: 2), use the following command:import biasondemand
biasondemand.generate_dataset(path='my_biased_dataset', dim=1000, l_m_y=1.5, l_h_r=2)Or, again, if you prefer the command line, just use:bias_on_demand_generate_dataset -p my_biased_dataset -dim 1000 -l_m_y 1.5 -l_h_r 2This will generate a biased dataset with 1000 rows and save it in the directorydatasets/my_biased_dataset/.The following command line arguments are available to specify properties of the dataset:dim: Dimension of the datasetsy: Standard deviation of the noise of Yl_q: Lambda coefficient for importance of Q for Yl_r_q: Lambda coefficient that quantifies the influence from R to Qthr_supp: Threshold correlation for discarding features too much correlated with sFurthermore, the following command line arguments are available to specify the types of biases to be introduced in the dataset:l_y: Lambda coefficient for historical bias on the target yl_m_y: Lambda coefficient for measurement bias on the target yl_h_r: Lambda coefficient for historical bias on Rl_h_q: Lambda coefficient for historical bias on Ql_m: Lambda coefficient for measurement bias on the feature R. If l_m!=0 P substitutes R.p_u: Percentage of undersampling instance with A=1l_r: Boolean for inducing representation bias, that is undersampling conditioning on a variable, e.g. Rl_o: Boolean variable for excluding an important variable (ommited variable bias), e.g. R (or its proxy)l_y_b: Lambda coefficient for interaction proxy bias, i.e., historical bias on the label y with lower values of y for individuals in group A=1 with high values for the feature RNotice that the biases are introduced w.r.t. idividuals in the group A=1.
For most types of bias, larger values mean more bias. The only exceptions are undersampling and representation bias (which can be seen as a specific type of undersampling conditional on the feature R) where smaller values correspond to more (conditional) undersampling, i.e., more bias.Run experiments using biasondemandIn the repohttps://github.com/rcrupiISP/BiasOnDemandwe provide the code and instructions to run a set of experiments for investigating bias, fairness, and mitigation techniques.
You can also check outour paperfor more details on this topic.Python versionBiasondemand requires Python 3.7 or later.CitationIf you use biasondemand in your research, please cite our paper:@inproceedings{baumann2023bias,
  title={Bias on Demand: A Modelling Framework That Generates Synthetic Data With Bias},
  author={Baumann, Joachim and Castelnovo, Alessandro and Crupi, Riccardo and Inverardi, Nicole and Regoli, Daniele},
  booktitle={Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency},
  doi={https://doi.org/10.1145/3593013.3594058},
  year={2023}
}