This repository contains the implementation of 'Automatic Location of Disparities' (ALD) for conducting algorithmic audits.

# install.packages("remotes") remotes::install_github("https://github.com/moritzvz/ald") 

ALD is dependent on several other packages for handling data, modeling, and generating reports: partykit, assertthat, magrittr, tidyselect, tibble, dplyr, tidyr, readr, rmarkdown, flextable, stringr, ggplot2, ggparty, cowplot, scales, hms

The ALD audit:

• is performed on a dataset of your choice that must be provided as a .csv file
• requires notion of fairness to be set to 'statistical parity' or 'equalized odds'

• in case of 'statistical parity' you must set the outcome_variable argument to the name of the outcome variable in your dataset
• for 'equalized odds' you must set the prediction_variable and ground_truth_variable arguments to the names of the prediction and ground truth variables in your dataset

• by default all other variables (not outcome, prediction, ground truth) in you dataset will be used as sensitive attributes in the audit. You can use the sensitive_attributes argument to specifically set the sensitive attributes to a subset of your dataset varaiables
• requires a ranking mechanism which must be 'confidence' or 'magnitude'
• requires a maximum number of groups in the report (n_grp)
• requires a number of trees to model in partykit::cforest (ntree)
• requires a alpha argument passed to partykit::cforest (alpha)
• optionally takes a p-value adjustment method to pass to stats::p.adjust (adjust_method), either "BH" (Benjamini-Hochberg, by default) or "bonferroni" (Bonferroni correction).
• optionally takes a random seed number that can be used for reproducibility of results
• writes a report to the directory that you set with the dir argument, with data_name argument used in the name

# for example ald_audit( file = "my_data.csv", prediction_variable = "prediction", ground_truth_variable = "ground_truth", notion_of_fairness = "equalized odds", ranking_mechanism = "confidence", data_name = "data_title", dir = here::here(""), n_grp = 3, ntree = 25, alpha = 0.1) ald_audit( file = "my_data.csv", outcome_variable = "outcome", notion_of_fairness = "statistical parity", ranking_mechanism = "confidence", data_name = "data_title", dir = here::here(""), n_grp = 3, ntree = 25, alpha = 0.1) 

Please consider citing us if you find this helpful for your work:

@inproceedings{vonZahn.2023, title={Locating disparities in machine learning}, author={von Zahn, Moritz and Hinz, Oliver and Feuerriegel, Stefan}, booktitle={2023 IEEE International Conference on Big Data (BigData)}, pages={1883--1894}, year={2023}, organization={IEEE} }