
@article{propublica,
author = {Julia {Angwin} and Jeff {Larson} and Surya {Mattu} and and Lauren {Kirchner}},
title = {Machine bias:
There’s software used across the country to predict future criminals. And it’s
biased against blacks},
journal = {ProPublica},
year = {2016},
url = {https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing}
}

@Article{ranger,
    title = {{ranger}: A Fast Implementation of Random Forests for High
      Dimensional Data in {C++} and {R}},
    author = {Marvin N. Wright and Andreas Ziegler},
    journal = {Journal of Statistical Software},
    year = {2017},
    volume = {77},
    number = {1},
    pages = {1--17},
    doi = {10.18637/jss.v077.i01},
  }

@article{dalex,
  author  = {Przemyslaw Biecek},
  title   = {DALEX: Explainers for Complex Predictive Models in R},
  journal = {Journal of Machine Learning Research},
  year    = {2018},
  volume  = {19},
  number  = {84},
  pages   = {1-5},
  url     = {https://jmlr.org/papers/v19/18-416.html}
}

@misc{adverseImpact,
author = {{Code of Federal Regulations}},
title = {SECTION 4D, UNIFORM GUIDELINES ON EMPLOYEE SELECTION PROCEDURES (1978)},
year = {1978},
url = {https://www.govinfo.gov/content/pkg/CFR-2014-title29-vol4/xml/CFR-2014-title29-vol4-part1607.xml},
}

@inproceedings{rubin,
author = {Verma, Sahil and Rubin, Julia},
title = {Fairness Definitions Explained},
year = {2018},
isbn = {9781450357463},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3194770.3194776},
doi = {10.1145/3194770.3194776},
abstract = {Algorithm fairness has started to attract the attention of researchers in AI, Software
Engineering and Law communities, with more than twenty different notions of fairness
proposed in the last few years. Yet, there is no clear agreement on which definition
to apply in each situation. Moreover, the detailed differences between multiple definitions
are difficult to grasp. To address this issue, this paper collects the most prominent
definitions of fairness for the algorithmic classification problem, explains the rationale
behind these definitions, and demonstrates each of them on a single unifying case-study.
Our analysis intuitively explains why the same case can be considered fair according
to some definitions and unfair according to others.},
booktitle = {Proceedings of the International Workshop on Software Fairness},
pages = {1–7},
numpages = {7},
location = {Gothenburg, Sweden},
series = {FairWare '18}
}



@inproceedings{disparateImpact,
author = {Feldman, Michael and Friedler, Sorelle A. and Moeller, John and Scheidegger, Carlos and Venkatasubramanian, Suresh},
title = {Certifying and Removing Disparate Impact},
year = {2015},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2783258.2783311},
booktitle = {Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {259–268},
numpages = {10},
keywords = {fairness, disparate impact, machine learning},
location = {Sydney, NSW, Australia},
series = {KDD '15}
}

@article{kamiran,
author = {Kamiran, Faisal and Calders, Toon},
year = {2011},
month = {10},
pages = {},
title = {Data Pre-Processing Techniques for Classification without Discrimination},
volume = {33},
journal = {Knowledge and Information Systems},
url={https://doi.org/10.1007/s10115-011-0463-8}
}


@INPROCEEDINGS{postKamiran,
  author={F. {Kamiran} and A. {Karim} and X. {Zhang}},
  booktitle={2012 IEEE 12th International Conference on Data Mining},
  title={Decision Theory for Discrimination-Aware Classification},
  year={2012},
  volume={},
  number={},
  pages={924-929},
  url={https://doi.org/10.1109/ICDM.2012.45}}

