@book{em:86,
  editor  = "Engelmore, Robert and Morgan, Anthony",
  title   = "Blackboard Systems",
  year    = 1986,
  address = "Reading, Mass.",
  publisher = "Addison-Wesley",
}

@article{washington2018argue,
  title={How to argue with an algorithm: Lessons from the COMPAS-ProPublica debate},
  author={Washington, Anne L},
  journal={Colo. Tech. LJ},
  volume={17},
  pages={131},
  year={2018},
  publisher={HeinOnline}
}

@article{bao2021s,
  title={It's compaslicated: The messy relationship between rai datasets and algorithmic fairness benchmarks},
  author={Bao, Michelle and Zhou, Angela and Zottola, Samantha and Brubach, Brian and Desmarais, Sarah and Horowitz, Aaron and Lum, Kristian and Venkatasubramanian, Suresh},
  journal={arXiv preprint arXiv:2106.05498},
  year={2021}
}


@article{gilardoni2006minimum,
  title={On the minimum f-divergence for given total variation},
  author={Gilardoni, Gustavo L},
  journal={Comptes rendus. Math{\'e}matique},
  volume={343},
  number={11-12},
  pages={763--766},
  year={2006}
}

@article{bertsimas2019adaptive,
  title={Adaptive distributionally robust optimization},
  author={Bertsimas, Dimitris and Sim, Melvyn and Zhang, Meilin},
  journal={Management Science},
  volume={65},
  number={2},
  pages={604--618},
  year={2019},
  publisher={INFORMS}
}

@article{namkoong2016stochastic,
  title={Stochastic gradient methods for distributionally robust optimization with f-divergences},
  author={Namkoong, Hongseok and Duchi, John C},
  journal={Advances in neural information processing systems},
  volume={29},
  year={2016}
}

@article{rahimian2019distributionally,
  title={Distributionally robust optimization: A review},
  author={Rahimian, Hamed and Mehrotra, Sanjay},
  journal={arXiv preprint arXiv:1908.05659},
  year={2019}
}

@inproceedings{asoodeh2015maximal,
  title={On maximal correlation, mutual information and data privacy},
  author={Asoodeh, Shahab and Alajaji, Fady and Linder, Tam{\'a}s},
  booktitle={2015 IEEE 14th Canadian workshop on information theory (CWIT)},
  pages={27--31},
  year={2015},
  organization={IEEE}
}


@inproceedings{c:83,
  author  = "Clancey, William J.",
  year    = 1983,
  title   = "{Communication, Simulation, and Intelligent
Agents: Implications of Personal Intelligent Machines
for Medical Education}",
  booktitle="Proceedings of the Eighth International Joint Conference on Artificial Intelligence {(IJCAI-83)}", 
  pages   = "556-560",
  address = "Menlo Park, Calif",
  publisher = "{IJCAI Organization}",
}
@inproceedings{c:84,
  author  = "Clancey, William J.",
  year    = 1984,
  title   = "{Classification Problem Solving}",
  booktitle = "Proceedings of the Fourth National 
              Conference on Artificial Intelligence",
  pages   = "45-54",
  address = "Menlo Park, Calif.",
  publisher="AAAI Press",
}
@article{r:80,
  author = {Robinson, Arthur L.},
  title = {New Ways to Make Microcircuits Smaller},
  volume = {208},
  number = {4447},
  pages = {1019--1022},
  year = {1980},
  doi = {10.1126/science.208.4447.1019},
  publisher = {American Association for the Advancement of Science},
  issn = {0036-8075},
  URL = {https://science.sciencemag.org/content/208/4447/1019},
  eprint = {https://science.sciencemag.org/content/208/4447/1019.full.pdf},
  journal = {Science},
}
@article{r:80x,
  author  = "Robinson, Arthur L.",
  year    = 1980,
  title   = "{New Ways to Make Microcircuits Smaller---Duplicate Entry}",
  journal = "Science",
  volume  =  208,
  pages   = "1019-1026",
}
@article{hcr:83,
title = {Strategic explanations for a diagnostic consultation system},
journal = {International Journal of Man-Machine Studies},
volume = {20},
number = {1},
pages = {3-19},
year = {1984},
issn = {0020-7373},
doi = {https://doi.org/10.1016/S0020-7373(84)80003-6},
url = {https://www.sciencedirect.com/science/article/pii/S0020737384800036},
author = {Diane Warner Hasling and William J. Clancey and Glenn Rennels},
abstract = {This article examines the problem of automatte explanation of reasoning, especially as it relates to expert systems. By explanation we mean the ability of a program to discuss what it is doing in some understandable way. We first present a general framework in which to view explanation and review some of the research done in this area. We then focus on the explanation system for NEOMYCIN, a medical consultation program. A consultation program interactively helps a user to solve a problem. Our goal is to have NEOMYCIN explain its problem-solving strategies. An explanation of strategy describes the plan the program is using to reach a solution. Such an explanation is usually concrete, referring to aspects of the current problem situation. Abstract explanations articulate a general principle, which can be applied in different situations; such explanations are useful in teaching and in explaining by analogy. We describe the aspects of NEOMYCIN that make abstract strategic explanations possible—the representation of strategic knowledge explicitly and separately from domain knowledge— and demonstrate how this representation can be used to generate explanations.}
}
@article{hcrt:83,
  author  = "Hasling, Diane Warner and Clancey, William J. and Rennels, Glenn R. and Test, Thomas",
  year    = 1983,
  title   = "{Strategic Explanations in Consultation---Duplicate}",
  journal = "The International Journal of Man-Machine Studies",
  volume  = 20,
  number  = 1,
  pages   = "3-19",
}
@techreport{r:86,
  author  = "Rice, James",
  year    = 1986,
  title   = "{Poligon: A System for Parallel Problem Solving}",
  type    = "Technical Report", 
  number  = "KSL-86-19", 
  institution = "Dept.\ of Computer Science, Stanford Univ.",
}
@phdthesis{c:79,
  author  = "Clancey, William J.",
  year    = 1979,
  title   = "{Transfer of Rule-Based Expertise
through a Tutorial Dialogue}",
  type    = "{Ph.D.} diss.",
  school  = "Dept.\ of Computer Science, Stanford Univ.",
  address = "Stanford, Calif.",
}
@unpublished{c:21,
  author  = "Clancey, William J.",
  title   = "{The Engineering of Qualitative Models}",
  year    = 2021,
  note    = "Forthcoming",
}
@misc{c:22,
      title={Attention Is All You Need}, 
      author={Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
      year={2017},
      eprint={1706.03762},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
@misc{c:23,
  title        = "Pluto: The 'Other' Red Planet",
  author       = "{NASA}",
  howpublished = "\url{https://www.nasa.gov/nh/pluto-the-other-red-planet}",
  year         = 2015,
  note         = "Accessed: 2018-12-06"
}

@inproceedings{cho2020afair,
  title={A fair classifier using mutual information},
  author={Cho, Jaewoong and Hwang, Gyeongjo and Suh, Changho},
  booktitle={2020 IEEE International Symposium on Information Theory (ISIT)},
  pages={2521--2526},
  year={2020},
  organization={IEEE}
}

@inproceedings{roh2020fr,
  title={Fr-train: A mutual information-based approach to fair and robust training},
  author={Roh, Yuji and Lee, Kangwook and Whang, Steven and Suh, Changho},
  booktitle={International Conference on Machine Learning},
  pages={8147--8157},
  year={2020},
  organization={PMLR}
}

@article{cho2020bfair,
  title={A fair classifier using kernel density estimation},
  author={Cho, Jaewoong and Hwang, Gyeongjo and Suh, Changho},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={15088--15099},
  year={2020}
}

@inproceedings{mary2019fairness,
  title={Fairness-aware learning for continuous attributes and treatments},
  author={Mary, J{\'e}r{\'e}mie and Calauzenes, Cl{\'e}ment and El Karoui, Noureddine},
  booktitle={International Conference on Machine Learning},
  pages={4382--4391},
  year={2019},
  organization={PMLR}
}

@article{baharlouei2019r,
  title={Renyi Fair Inference},
  author={Baharlouei, Sina and Nouiehed, Maher and Beirami, Ahmad and Razaviyayn, Meisam},
  journal={arXiv preprint arXiv:1906.12005},
  year={2019}
}

@article{lowy2022stochastic,
  title={A stochastic optimization framework for fair risk minimization},
  author={Lowy, Andrew and Baharlouei, Sina and Pavan, Rakesh and Razaviyayn, Meisam and Beirami, Ahmad},
  journal={Transactions on Machine Learning Research},
  year={2022}
}

@article{corbett2018measure,
  title={The measure and mismeasure of fairness: A critical review of fair machine learning},
  author={Corbett-Davies, Sam and Goel, Sharad},
  journal={arXiv preprint arXiv:1808.00023},
  year={2018}
}

@article{bonchi2017exposing,
  title={Exposing the probabilistic causal structure of discrimination},
  author={Bonchi, Francesco and Hajian, Sara and Mishra, Bud and Ramazzotti, Daniele},
  journal={International Journal of Data Science and Analytics},
  volume={3},
  pages={1--21},
  year={2017},
  publisher={Springer}
}

@inproceedings{grgic2016case,
  title={The case for process fairness in learning: Feature selection for fair decision making},
  author={Grgic-Hlaca, Nina and Zafar, Muhammad Bilal and Gummadi, Krishna P and Weller, Adrian},
  booktitle={NIPS symposium on machine learning and the law},
  volume={1},
  number={2},
  pages={11},
  year={2016},
  organization={Barcelona, Spain}
}

@article{johnson2016impartial,
  title={Impartial predictive modeling: Ensuring fairness in arbitrary models},
  author={Johnson, Kory D and Foster, Dean P and Stine, Robert A},
  journal={Statistical Science},
  pages={1},
  year={2016}
}

@misc{qureshi2016causal,
  title={Causal discrimination discovery through propensity score analysis},
  author={Qureshi, Bilal and Kamiran, Faisal and Karim, Asim and Ruggieri, Salvatore},
  journal={arXiv preprint arXiv:1608.03735},
  volume={12},
  year={2016},
  publisher={Aug}
}

@inproceedings{feldman2015certifying,
  title={Certifying and removing disparate impact},
  author={Feldman, Michael and Friedler, Sorelle A and Moeller, John and Scheidegger, Carlos and Venkatasubramanian, Suresh},
  booktitle={proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining},
  pages={259--268},
  year={2015}
}

@article{hardt2016equality,
  title={Equality of opportunity in supervised learning},
  author={Hardt, Moritz and Price, Eric and Srebro, Nati},
  journal={Advances in neural information processing systems},
  volume={29},
  year={2016}
}

@inproceedings{agarwal2018reductions,
  title={A reductions approach to fair classification},
  author={Agarwal, Alekh and Beygelzimer, Alina and Dud{\'\i}k, Miroslav and Langford, John and Wallach, Hanna},
  booktitle={International Conference on Machine Learning},
  pages={60--69},
  year={2018},
  organization={PMLR}
}

@article{berk2021fairness,
  title={Fairness in criminal justice risk assessments: The state of the art},
  author={Berk, Richard and Heidari, Hoda and Jabbari, Shahin and Kearns, Michael and Roth, Aaron},
  journal={Sociological Methods \& Research},
  volume={50},
  number={1},
  pages={3--44},
  year={2021},
  publisher={Sage Publications Sage CA: Los Angeles, CA}
}

@book{berk2012criminal,
  title={Criminal justice forecasts of risk: A machine learning approach},
  author={Berk, Richard},
  year={2012},
  publisher={Springer Science \& Business Media}
}

@article{skeem2016risk,
  title={Risk, race, and recidivism: Predictive bias and disparate impact},
  author={Skeem, Jennifer L and Lowenkamp, Christopher T},
  journal={Criminology},
  volume={54},
  number={4},
  pages={680--712},
  year={2016},
  publisher={Wiley Online Library}
}

@article{chouldechova2017fair,
  title={Fair prediction with disparate impact: A study of bias in recidivism prediction instruments},
  author={Chouldechova, Alexandra},
  journal={Big data},
  volume={5},
  number={2},
  pages={153--163},
  year={2017},
  publisher={Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA}
}

@article{edwards2015censoring,
  title={Censoring representations with an adversary},
  author={Edwards, Harrison and Storkey, Amos},
  journal={arXiv preprint arXiv:1511.05897},
  year={2015}
}

@article{kamiran2013quantifying,
  title={Quantifying explainable discrimination and removing illegal discrimination in automated decision making},
  author={Kamiran, Faisal and {\v{Z}}liobait{\.e}, Indr{\.e} and Calders, Toon},
  journal={Knowledge and information systems},
  volume={35},
  pages={613--644},
  year={2013},
  publisher={Springer}
}

@inproceedings{zafar2017fairness,
  title={Fairness constraints: Mechanisms for fair classification},
  author={Zafar, Muhammad Bilal and Valera, Isabel and Rogriguez, Manuel Gomez and Gummadi, Krishna P},
  booktitle={Artificial intelligence and statistics},
  pages={962--970},
  year={2017},
  organization={PMLR}
}

@inproceedings{kamishima2011fairness,
  title={Fairness-aware learning through regularization approach},
  author={Kamishima, Toshihiro and Akaho, Shotaro and Sakuma, Jun},
  booktitle={2011 IEEE 11th International Conference on Data Mining Workshops},
  pages={643--650},
  year={2011},
  organization={IEEE}
}

@inproceedings{rezaei2020fairness,
  title={Fairness for robust log loss classification},
  author={Rezaei, Ashkan and Fathony, Rizal and Memarrast, Omid and Ziebart, Brian},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={04},
  pages={5511--5518},
  year={2020}
}

@inproceedings{zhang2018mitigating,
  title={Mitigating unwanted biases with adversarial learning},
  author={Zhang, Brian Hu and Lemoine, Blake and Mitchell, Margaret},
  booktitle={Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society},
  pages={335--340},
  year={2018}
}

@inproceedings{beutel2019putting,
  title={Putting fairness principles into practice: Challenges, metrics, and improvements},
  author={Beutel, Alex and Chen, Jilin and Doshi, Tulsee and Qian, Hai and Woodruff, Allison and Luu, Christine and Kreitmann, Pierre and Bischof, Jonathan and Chi, Ed H},
  booktitle={Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society},
  pages={453--459},
  year={2019}
}

@article{bechavod2017penalizing,
  title={Penalizing unfairness in binary classification},
  author={Bechavod, Yahav and Ligett, Katrina},
  journal={arXiv preprint arXiv:1707.00044},
  year={2017}
}

@inproceedings{perez2017fair,
  title={Fair kernel learning},
  author={P{\'e}rez-Suay, Adri{\'a}n and Laparra, Valero and Mateo-Garc{\'\i}a, Gonzalo and Mu{\~n}oz-Mar{\'\i}, Jordi and G{\'o}mez-Chova, Luis and Camps-Valls, Gustau},
  booktitle={Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18--22, 2017, Proceedings, Part I},
  pages={339--355},
  year={2017},
  organization={Springer}
}

@inproceedings{dwork2012fairness,
  title={Fairness through awareness},
  author={Dwork, Cynthia and Hardt, Moritz and Pitassi, Toniann and Reingold, Omer and Zemel, Richard},
  booktitle={Proceedings of the 3rd innovations in theoretical computer science conference},
  pages={214--226},
  year={2012}
}
@article{prost2019toward,
  title={Toward a better trade-off between performance and fairness with kernel-based distribution matching},
  author={Prost, Flavien and Qian, Hai and Chen, Qiuwen and Chi, Ed H and Chen, Jilin and Beutel, Alex},
  journal={arXiv preprint arXiv:1910.11779},
  year={2019}
}

@article{grari2019fairness,
  title={Fairness-Aware Neural Renyi Minimization for Continuous Features},
  author={Grari, Vincent and Ruf, Boris and Lamprier, Sylvain and Detyniecki, Marcin},
  journal={arXiv preprint arXiv:1911.04929},
  year={2019}
}

@inproceedings{grari2021learning,
  title={Learning unbiased representations via r{\'e}nyi minimization},
  author={Grari, Vincent and Hajouji, Oualid El and Lamprier, Sylvain and Detyniecki, Marcin},
  booktitle={Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13--17, 2021, Proceedings, Part II 21},
  pages={749--764},
  year={2021},
  organization={Springer}
}

@inproceedings{zemel2013learning,
  title={Learning fair representations},
  author={Zemel, Rich and Wu, Yu and Swersky, Kevin and Pitassi, Toni and Dwork, Cynthia},
  booktitle={International conference on machine learning},
  pages={325--333},
  year={2013},
  organization={PMLR}
}

@article{calmon2017optimized,
  title={Optimized pre-processing for discrimination prevention},
  author={Calmon, Flavio and Wei, Dennis and Vinzamuri, Bhanukiran and Natesan Ramamurthy, Karthikeyan and Varshney, Kush R},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@article{pleiss2017fairness,
  title={On fairness and calibration},
  author={Pleiss, Geoff and Raghavan, Manish and Wu, Felix and Kleinberg, Jon and Weinberger, Kilian Q},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@inproceedings{kamiran2010discrimination,
  title={Discrimination aware decision tree learning},
  author={Kamiran, Faisal and Calders, Toon and Pechenizkiy, Mykola},
  booktitle={2010 IEEE international conference on data mining},
  pages={869--874},
  year={2010},
  organization={IEEE}
}

@inproceedings{raff2018fair,
  title={Fair forests: Regularized tree induction to minimize model bias},
  author={Raff, Edward and Sylvester, Jared and Mills, Steven},
  booktitle={Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society},
  pages={243--250},
  year={2018}
}

@inproceedings{aghaei2019learning,
  title={Learning optimal and fair decision trees for non-discriminative decision-making},
  author={Aghaei, Sina and Azizi, Mohammad Javad and Vayanos, Phebe},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={33},
  number={01},
  pages={1418--1426},
  year={2019}
}

@article{donini2018empirical,
  title={Empirical risk minimization under fairness constraints},
  author={Donini, Michele and Oneto, Luca and Ben-David, Shai and Shawe-Taylor, John S and Pontil, Massimiliano},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}

@inproceedings{fish2015fair,
  title={Fair boosting: a case study},
  author={Fish, Benjamin and Kun, Jeremy and Lelkes, Ad{\'a}m D},
  booktitle={Workshop on Fairness, Accountability, and Transparency in Machine Learning},
  year={2015},
  organization={Citeseer Princeton}
}

@article{berk2017convex,
  title={A convex framework for fair regression},
  author={Berk, Richard and Heidari, Hoda and Jabbari, Shahin and Joseph, Matthew and Kearns, Michael and Morgenstern, Jamie and Neel, Seth and Roth, Aaron},
  journal={arXiv preprint arXiv:1706.02409},
  year={2017}
}

@article{taskesen2020distributionally,
  title={A distributionally robust approach to fair classification},
  author={Taskesen, Bahar and Nguyen, Viet Anh and Kuhn, Daniel and Blanchet, Jose},
  journal={arXiv preprint arXiv:2007.09530},
  year={2020}
}

@article{chzhen2022minimax,
  title={A minimax framework for quantifying risk-fairness trade-off in regression},
  author={Chzhen, Evgenii and Schreuder, Nicolas},
  journal={The Annals of Statistics},
  volume={50},
  number={4},
  pages={2416--2442},
  year={2022},
  publisher={Institute of Mathematical Statistics}
}

@inproceedings{jiang2020wasserstein,
  title={Wasserstein fair classification},
  author={Jiang, Ray and Pacchiano, Aldo and Stepleton, Tom and Jiang, Heinrich and Chiappa, Silvia},
  booktitle={Uncertainty in artificial intelligence},
  pages={862--872},
  year={2020},
  organization={PMLR}
}

@article{sweeney2013discrimination,
  title={Discrimination in online ad delivery},
  author={Sweeney, Latanya},
  journal={Communications of the ACM},
  volume={56},
  number={5},
  pages={44--54},
  year={2013},
  publisher={ACM New York, NY, USA}
}

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

@article{liu2018large,
  title={Large-scale celebfaces attributes (celeba) dataset},
  author={Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
  journal={Retrieved August},
  volume={15},
  number={2018},
  pages={11},
  year={2018}
}

@inproceedings{hebert2018multicalibration,
  title={Multicalibration: Calibration for the (computationally-identifiable) masses},
  author={H{\'e}bert-Johnson, Ursula and Kim, Michael and Reingold, Omer and Rothblum, Guy},
  booktitle={International Conference on Machine Learning},
  pages={1939--1948},
  year={2018},
  organization={PMLR}
}

@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}
}

@inproceedings{hashimoto2018fairness,
  title={Fairness without demographics in repeated loss minimization},
  author={Hashimoto, Tatsunori and Srivastava, Megha and Namkoong, Hongseok and Liang, Percy},
  booktitle={International Conference on Machine Learning},
  pages={1929--1938},
  year={2018},
  organization={PMLR}
}

@article{wang2020robust,
  title={Robust optimization for fairness with noisy protected groups},
  author={Wang, Serena and Guo, Wenshuo and Narasimhan, Harikrishna and Cotter, Andrew and Gupta, Maya and Jordan, Michael},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={5190--5203},
  year={2020}
}

@article{lahoti2020fairness,
  title={Fairness without demographics through adversarially reweighted learning},
  author={Lahoti, Preethi and Beutel, Alex and Chen, Jilin and Lee, Kang and Prost, Flavien and Thain, Nithum and Wang, Xuezhi and Chi, Ed},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={728--740},
  year={2020}
}

@article{chen2018my,
  title={Why is my classifier discriminatory?},
  author={Chen, Irene and Johansson, Fredrik D and Sontag, David},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}

@article{chouldechova2018frontiers,
  title={The frontiers of fairness in machine learning},
  author={Chouldechova, Alexandra and Roth, Aaron},
  journal={arXiv preprint arXiv:1810.08810},
  year={2018}
}

@inproceedings{liu2022partial,
  title={Partial-Quasi-Newton Methods: Efficient Algorithms for Minimax Optimization Problems with Unbalanced Dimensionality},
  author={Liu, Chengchang and Bi, Shuxian and Luo, Luo and Lui, John CS},
  booktitle={Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={1031--1041},
  year={2022}
}

@inproceedings{mcmahan2017communication,
  title={Communication-efficient learning of deep networks from decentralized data},
  author={McMahan, Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and y Arcas, Blaise Aguera},
  booktitle={Artificial intelligence and statistics},
  pages={1273--1282},
  year={2017},
  organization={PMLR}
}

@inproceedings{iosifidis2020online,
  title={-Online Fairness-Aware Learning Under Class Imbalance},
  author={Iosifidis, Vasileios and Ntoutsi, Eirini},
  booktitle={International Conference on Discovery Science},
  pages={159--174},
  year={2020},
  organization={Springer}
}

@article{subramanian2021fairness,
  title={Fairness-aware class imbalanced learning},
  author={Subramanian, Shivashankar and Rahimi, Afshin and Baldwin, Timothy and Cohn, Trevor and Frermann, Lea},
  journal={arXiv preprint arXiv:2109.10444},
  year={2021}
}

@article{deng2022fifa,
  title={Fifa: Making fairness more generalizable in classifiers trained on imbalanced data},
  author={Deng, Zhun and Zhang, Jiayao and Zhang, Linjun and Ye, Ting and Coley, Yates and Su, Weijie J and Zou, James},
  journal={arXiv preprint arXiv:2206.02792},
  year={2022}
}

@inproceedings{tarzanagh2023fairness,
  title={Fairness-aware class imbalanced learning on multiple subgroups},
  author={Tarzanagh, Davoud Ataee and Hou, Bojian and Tong, Boning and Long, Qi and Shen, Li},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={2123--2133},
  year={2023},
  organization={PMLR}
}

@inproceedings{lin2017focal,
  title={Focal loss for dense object detection},
  author={Lin, Tsung-Yi and Goyal, Priya and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={2980--2988},
  year={2017}
}

@article{cao2019learning,
  title={Learning imbalanced datasets with label-distribution-aware margin loss},
  author={Cao, Kaidi and Wei, Colin and Gaidon, Adrien and Arechiga, Nikos and Ma, Tengyu},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}