@misc{peychev2022latent,
      title={Latent Space Smoothing for Individually Fair Representations}, 
      author={Momchil Peychev and Anian Ruoss and Mislav Balunović and Maximilian Baader and Martin Vechev},
      year={2022},
      eprint={2111.13650},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@inproceedings{kearns2018preventing,
  title={Preventing fairness gerrymandering: Auditing and learning for subgroup fairness},
  author={Kearns, Michael and Neel, Seth and Roth, Aaron and Wu, Zhiwei Steven},
  booktitle={International Conference on Machine Learning},
  pages={2564--2572},
  year={2018},
  organization={PMLR}
}
@article{yurochkin2019training,
  title={Training individually fair ML models with sensitive subspace robustness},
  author={Yurochkin, Mikhail and Bower, Amanda and Sun, Yuekai},
  journal={arXiv preprint arXiv:1907.00020},
  year={2019}
}
@inproceedings{liu2015deep,
  title={Deep learning face attributes in the wild},
  author={Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={3730--3738},
  year={2015}
}
@inproceedings{karkkainen2021fairface,
  title={Fairface: Face attribute dataset for balanced race, gender, and age for bias measurement and mitigation},
  author={Karkkainen, Kimmo and Joo, Jungseock},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={1548--1558},
  year={2021}
}
@article{ruoss2020learning,
  title={Learning certified individually fair representations},
  author={Ruoss, Anian and Balunovic, Mislav and Fischer, Marc and Vechev, Martin},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={7584--7596},
  year={2020}
}
@inproceedings{ramaswamy2021fair,
  title={Fair attribute classification through latent space de-biasing},
  author={Ramaswamy, Vikram V and Kim, Sunnie SY and Russakovsky, Olga},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={9301--9310},
  year={2021}
}

% Loan applications

@article{khandaniloans,
title = {Consumer credit-risk models via machine-learning algorithms},
author = {Khandani, Amir E. and Kim, Adlar J. and Lo, Andrew},
year = {2010},
journal = {Journal of Banking And Finance},
volume = {34},
number = {11},
pages = {2767-2787},
keywords = {Household behavior Consumer credit risk Credit card borrowing Machine learning Nonparametric estimation},
url = {https://EconPapers.repec.org/RePEc:eee:jbfina:v:34:y:2010:i:11:p:2767-2787}
}

% Crime assessment

@article{brennancrimes,
author = {Brennan, Tim and Dieterich, William and Ehret, Beate},
year = {2009},
month = {01},
pages = {21-40},
title = {Evaluating the predictive validity of the COMPAS Risk and Needs Assessment System},
volume = {36},
journal = {Criminal Justice and Behavior - CRIM JUSTICE BEHAV},
doi = {10.1177/0093854808326545}
}

% Human resources

@article{tambehr,
  title={Artificial intelligence in human resources management: Challenges and a path forward},
  author={Tambe, Prasanna and Cappelli, Peter and Yakubovich, Valery},
  journal={California Management Review},
  volume={61},
  number={4},
  pages={15--42},
  year={2019},
  publisher={SAGE Publications Sage CA: Los Angeles, CA}
}

% Bias in AI models

@inproceedings{buolamwini2018gender,
  title={Gender shades: Intersectional accuracy disparities in commercial gender classification},
  author={Buolamwini, Joy and Gebru, Timnit},
  booktitle={Conference on fairness, accountability and transparency},
  pages={77--91},
  year={2018},
  organization={PMLR}
}

@article{klare2012face,
  title={Face recognition performance: Role of demographic information},
  author={Klare, Brendan F and Burge, Mark J and Klontz, Joshua C and Bruegge, Richard W Vorder and Jain, Anil K},
  journal={IEEE Transactions on information forensics and security},
  volume={7},
  number={6},
  pages={1789--1801},
  year={2012},
  publisher={IEEE}
}

% Discrimination based on race and gender in AI
@article{hleg2019ethics,
  title={Ethics guidelines for trustworthy AI},
  author={Hleg, AI},
  journal={B-1049 Brussels},
  year={2019}
}

@article{act2021proposal,
  title={Proposal for a regulation of the European Parliament and the Council laying down harmonised rules on Artificial Intelligence (Artificial Intelligence Act) and amending certain Union legislative acts},
  author={Act, Artificial Intelligence},
  journal={EUR-Lex-52021PC0206},
  year={2021}
}

@article{jillson2021aiming,
  title={Aiming for truth, fairness, and equity in your company’s use of AI},
  author={Jillson, Elisa},
  journal={Federal Trade Commission},
  year={2021}
}

% Generative models

@inproceedings{kingmaglow,
 author = {Kingma, Durk P and Dhariwal, Prafulla},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Glow: Generative Flow with Invertible 1x1 Convolutions},
 url = {https://proceedings.neurips.cc/paper/2018/file/d139db6a236200b21cc7f752979132d0-Paper.pdf},
 volume = {31},
 year = {2018}
}

% Scalable certification of deep models / Randomized smoothing

@InProceedings{cohendeeplearning,
  title = 	 {Certified Adversarial Robustness via Randomized Smoothing},
  author =       {Cohen, Jeremy and Rosenfeld, Elan and Kolter, Zico},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {1310--1320},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {6},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/cohen19c/cohen19c.pdf},
  url = 	 {https://proceedings.mlr.press/v97/cohen19c.html},
}

% Individual fairness

@article{dworkindfairness,
  author    = {Cynthia Dwork and
               Moritz Hardt and
               Toniann Pitassi and
               Omer Reingold and
               Richard S. Zemel},
  title     = {Fairness Through Awareness},
  journal   = {CoRR},
  volume    = {abs/1104.3913},
  year      = {2011},
  url       = {http://arxiv.org/abs/1104.3913},
  eprinttype = {arXiv},
  eprint    = {1104.3913},
  timestamp = {Mon, 13 Aug 2018 16:48:46 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1104-3913.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

% Center smoothing

@inproceedings{kumarcentersmoothing,
 author = {Kumar, Aounon and Goldstein, Tom},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
 pages = {5560--5575},
 publisher = {Curran Associates, Inc.},
 title = {Center Smoothing: Certified Robustness for Networks with Structured Outputs},
 url = {https://proceedings.neurips.cc/paper/2021/file/2be8328f41144106f7144802f2367487-Paper.pdf},
 volume = {34},
 year = {2021}
}


