references.bib

@book{sadowski2019rethinking,
	title={Rethinking Productivity in Software Engineering},
	author={Sadowski, C. and Zimmermann, T.},
	isbn={9781484242216},
	url={https://books.google.co.uk/books?id=qcSWDwAAQBAJ},
	year={2019},
	publisher={Apress}
}

@inproceedings{zhang2018adversarial,
	author = {Zhang, Brian Hu and Lemoine, Blake and Mitchell, Margaret},
	title = {Mitigating Unwanted Biases with Adversarial Learning},
	year = {2018},
	isbn = {9781450360128},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/3278721.3278779},
	doi = {10.1145/3278721.3278779},
	abstract = {Machine learning is a tool for building models that accurately represent input training data. When undesired biases concerning demographic groups are in the training data, well-trained models will reflect those biases. We present a framework for mitigating such biases by including a variable for the group of interest and simultaneously learning a predictor and an adversary. The input to the network X, here text or census data, produces a prediction Y, such as an analogy completion or income bracket, while the adversary tries to model a protected variable Z, here gender or zip code. The objective is to maximize the predictor's ability to predict Y while minimizing the adversary's ability to predict Z. Applied to analogy completion, this method results in accurate predictions that exhibit less evidence of stereotyping Z. When applied to a classification task using the UCI Adult (Census) Dataset, it results in a predictive model that does not lose much accuracy while achieving very close to equality of odds (Hardt, et al., 2016). The method is flexible and applicable to multiple definitions of fairness as well as a wide range of gradient-based learning models, including both regression and classification tasks.},
	booktitle = {Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society},
	pages = {335–340},
	numpages = {6},
	keywords = {unbiasing, multi-task learning, debiasing, adversarial learning},
	location = {New Orleans, LA, USA},
	series = {AIES '18}
}
@inproceedings{kearns2019gerry,
	author = {Kearns, Michael and Neel, Seth and Roth, Aaron and Wu, Zhiwei Steven},
	title = {An Empirical Study of Rich Subgroup Fairness for Machine Learning},
	year = {2019},
	isbn = {9781450361255},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/3287560.3287592},
	doi = {10.1145/3287560.3287592},
	abstract = {Kearns, Neel, Roth, and Wu [ICML 2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across protected groups), but then asks that this constraint hold over an exponentially or infinitely large collection of subgroups defined by a class of functions with bounded VC dimension. They give an algorithm guaranteed to learn subject to this constraint, under the condition that it has access to oracles for perfectly learning absent a fairness constraint. In this paper, we undertake an extensive empirical evaluation of the algorithm of Kearns et al. On four real datasets for which fairness is a concern, we investigate the basic convergence of the algorithm when instantiated with fast heuristics in place of learning oracles, measure the tradeoffs between fairness and accuracy, and compare this approach with the recent algorithm of Agarwal, Beygelzeimer, Dudik, Langford, and Wallach [ICML 2018], which implements weaker and more traditional marginal fairness constraints defined by individual protected attributes. We find that in general, the Kearns et al. algorithm converges quickly, large gains in fairness can be obtained with mild costs to accuracy, and that optimizing accuracy subject only to marginal fairness leads to classifiers with substantial subgroup unfairness. We also provide a number of analyses and visualizations of the dynamics and behavior of the Kearns et al. algorithm. Overall we find this algorithm to be effective on real data, and rich subgroup fairness to be a viable notion in practice.},
	booktitle = {Proceedings of the Conference on Fairness, Accountability, and Transparency},
	pages = {100–109},
	numpages = {10},
	keywords = {Fairness Auditing, Algorithmic Bias, Subgroup Fairness, Fair Classification},
	location = {Atlanta, GA, USA},
	series = {FAT* '19}
}

@InProceedings{kearns2018gerry,
	title = 	 {Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness},
	author =       {Kearns, Michael and Neel, Seth and Roth, Aaron and Wu, Zhiwei Steven},
	booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
	pages = 	 {2564--2572},
	year = 	 {2018},
	editor = 	 {Dy, Jennifer and Krause, Andreas},
	volume = 	 {80},
	series = 	 {Proceedings of Machine Learning Research},
	month = 	 {10--15 Jul},
	publisher =    {PMLR},
	pdf = 	 {http://proceedings.mlr.press/v80/kearns18a/kearns18a.pdf},
	url = 	 {https://proceedings.mlr.press/v80/kearns18a.html},
	abstract = 	 {The most prevalent notions of fairness in machine learning fix a small collection of pre-defined groups (such as race or gender), and then ask for approximate parity of some statistic of the classifier (such as false positive rate) across these groups. Constraints of this form are susceptible to fairness gerrymandering, in which a classifier is fair on each individual group, but badly violates the fairness constraint on structured subgroups, such as certain combinations of protected attribute values. We thus consider fairness across exponentially or infinitely many subgroups, defined by a structured class of functions over the protected attributes. We first prove that the problem of auditing subgroup fairness for both equality of false positive rates and statistical parity is computationally equivalent to the problem of weak agnostic learning — which means it is hard in the worst case, even for simple structured subclasses. However, it also suggests that common heuristics for learning can be applied to successfully solve the auditing problem in practice. We then derive an algorithm that provably converges in a polynomial number of steps to the best subgroup-fair distribution over classifiers, given access to an oracle which can solve the agnostic learning problem. The algorithm is based on a formulation of subgroup fairness as a zero-sum game between a Learner (the primal player) and an Auditor (the dual player). We implement a variant of this algorithm using heuristic oracles, and show that we can effectively both audit and learn fair classifiers on a real dataset.}
}

@InProceedings{agarwal18grid,
	title = 	 {A Reductions Approach to Fair Classification},
	author =       {Agarwal, Alekh and Beygelzimer, Alina and Dudik, Miroslav and Langford, John and Wallach, Hanna},
	booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
	pages = 	 {60--69},
	year = 	 {2018},
	editor = 	 {Dy, Jennifer and Krause, Andreas},
	volume = 	 {80},
	series = 	 {Proceedings of Machine Learning Research},
	month = 	 {10--15 Jul},
	publisher =    {PMLR},
	pdf = 	 {http://proceedings.mlr.press/v80/agarwal18a/agarwal18a.pdf},
	url = 	 {https://proceedings.mlr.press/v80/agarwal18a.html},
	abstract = 	 {We present a systematic approach for achieving fairness in a binary classification setting. While we focus on two well-known quantitative definitions of fairness, our approach encompasses many other previously studied definitions as special cases. The key idea is to reduce fair classification to a sequence of cost-sensitive classification problems, whose solutions yield a randomized classifier with the lowest (empirical) error subject to the desired constraints. We introduce two reductions that work for any representation of the cost-sensitive classifier and compare favorably to prior baselines on a variety of data sets, while overcoming several of their disadvantages.}
}

@InProceedings{agarwal19grid,
	title = 	 {Fair Regression: Quantitative Definitions and Reduction-Based Algorithms},
	author =       {Agarwal, Alekh and Dudik, Miroslav and Wu, Zhiwei Steven},
	booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
	pages = 	 {120--129},
	year = 	 {2019},
	editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
	volume = 	 {97},
	series = 	 {Proceedings of Machine Learning Research},
	month = 	 {09--15 Jun},
	publisher =    {PMLR},
	pdf = 	 {http://proceedings.mlr.press/v97/agarwal19d/agarwal19d.pdf},
	url = 	 {https://proceedings.mlr.press/v97/agarwal19d.html},
	abstract = 	 {In this paper, we study the prediction of a real-valued target, such as a risk score or recidivism rate, while guaranteeing a quantitative notion of fairness with respect to a protected attribute such as gender or race. We call this class of problems fair regression. We propose general schemes for fair regression under two notions of fairness: (1) statistical parity, which asks that the prediction be statistically independent of the protected attribute, and (2) bounded group loss, which asks that the prediction error restricted to any protected group remain below some pre-determined level. While we only study these two notions of fairness, our schemes are applicable to arbitrary Lipschitz-continuous losses, and so they encompass least-squares regression, logistic regression, quantile regression, and many other tasks. Our schemes only require access to standard risk minimization algorithms (such as standard classification or least-squares regression) while providing theoretical guarantees on the optimality and fairness of the obtained solutions. In addition to analyzing theoretical properties of our schemes, we empirically demonstrate their ability to uncover fairness–accuracy frontiers on several standard datasets.}
}


@inproceedings{celis2019metafair,
	author = {Celis, L. Elisa and Huang, Lingxiao and Keswani, Vijay and Vishnoi, Nisheeth K.},
	title = {Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees},
	year = {2019},
	isbn = {9781450361255},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/3287560.3287586},
	doi = {10.1145/3287560.3287586},
	abstract = {Developing classification algorithms that are fair with respect to sensitive attributes of the data is an important problem due to the increased deployment of classification algorithms in societal contexts. Several recent works have focused on studying classification with respect to specific fairness metrics, modeled the corresponding fair classification problem as constrained optimization problems, and developed tailored algorithms to solve them. Despite this, there still remain important metrics for which there are no fair classifiers with theoretical guarantees; primarily because the resulting optimization problem is non-convex. The main contribution of this paper is a meta-algorithm for classification that can take as input a general class of fairness constraints with respect to multiple non-disjoint and multi-valued sensitive attributes, and which comes with provable guarantees. In particular, our algorithm can handle non-convex "linear fractional" constraints (which includes fairness constraints such as predictive parity) for which no prior algorithm was known. Key to our results is an algorithm for a family of classification problems with convex constraints along with a reduction from classification problems with linear fractional constraints to this family. Empirically, we observe that our algorithm is fast, can achieve near-perfect fairness with respect to various fairness metrics, and the loss in accuracy due to the imposed fairness constraints is often small.},
	booktitle = {Proceedings of the Conference on Fairness, Accountability, and Transparency},
	pages = {319–328},
	numpages = {10},
	keywords = {Classification, Algorithmic Fairness},
	location = {Atlanta, GA, USA},
	series = {FAT* '19}
}

@inproceedings{friedler2019fairness,
	author = {Friedler, Sorelle A. and Scheidegger, Carlos and Venkatasubramanian, Suresh and Choudhary, Sonam and Hamilton, Evan P. and Roth, Derek},
	title = {A Comparative Study of Fairness-Enhancing Interventions in Machine Learning},
	year = {2019},
	isbn = {9781450361255},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/3287560.3287589},
	doi = {10.1145/3287560.3287589},
	abstract = {Computers are increasingly used to make decisions that have significant impact on people's lives. Often, these predictions can affect different population subgroups disproportionately. As a result, the issue of fairness has received much recent interest, and a number of fairness-enhanced classifiers have appeared in the literature. This paper seeks to study the following questions: how do these different techniques fundamentally compare to one another, and what accounts for the differences? Specifically, we seek to bring attention to many under-appreciated aspects of such fairness-enhancing interventions that require investigation for these algorithms to receive broad adoption.We present the results of an open benchmark we have developed that lets us compare a number of different algorithms under a variety of fairness measures and existing datasets. We find that although different algorithms tend to prefer specific formulations of fairness preservations, many of these measures strongly correlate with one another. In addition, we find that fairness-preserving algorithms tend to be sensitive to fluctuations in dataset composition (simulated in our benchmark by varying training-test splits) and to different forms of preprocessing, indicating that fairness interventions might be more brittle than previously thought.},
	booktitle = {Proceedings of the Conference on Fairness, Accountability, and Transparency},
	pages = {329–338},
	numpages = {10},
	keywords = {Fairness-aware machine learning, benchmarks},
	location = {Atlanta, GA, USA},
	series = {FAT* '19}
}

@inproceedings{kusner2017counterfactual,
	author = {Kusner, Matt J and Loftus, Joshua and Russell, Chris and Silva, Ricardo},
	booktitle = {Advances in Neural Information Processing Systems},
	editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
	pages = {},
	publisher = {Curran Associates, Inc.},
	title = {Counterfactual Fairness},
	url = {https://proceedings.neurips.cc/paper/2017/file/a486cd07e4ac3d270571622f4f316ec5-Paper.pdf},
	volume = {30},
	year = {2017}
}

@article{bantilan2018themis,
	author = {Niels Bantilan},
	title = {Themis-ml: A Fairness-Aware Machine Learning Interface for End-To-End Discrimination Discovery and Mitigation},
	journal = {Journal of Technology in Human Services},
	volume = {36},
	number = {1},
	pages = {15-30},
	year  = {2018},
	publisher = {Routledge},
	doi = {10.1080/15228835.2017.1416512},
	URL = { 
	https://doi.org/10.1080/15228835.2017.1416512
	},
	eprint = { 
	https://doi.org/10.1080/15228835.2017.1416512
	
	}	
}

@inproceedings{galhotra2017themis,
	author = {Galhotra, Sainyam and Brun, Yuriy and Meliou, Alexandra},
	title = {Fairness Testing: Testing Software for Discrimination},
	year = {2017},
	isbn = {9781450351058},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/3106237.3106277},
	doi = {10.1145/3106237.3106277},
	abstract = { This paper defines software fairness and discrimination and develops a testing-based method for measuring if and how much software discriminates, focusing on causality in discriminatory behavior. Evidence of software discrimination has been found in modern software systems that recommend criminal sentences, grant access to financial products, and determine who is allowed to participate in promotions. Our approach, Themis, generates efficient test suites to measure discrimination. Given a schema describing valid system inputs, Themis generates discrimination tests automatically and does not require an oracle. We evaluate Themis on 20 software systems, 12 of which come from prior work with explicit focus on avoiding discrimination. We find that (1) Themis is effective at discovering software discrimination, (2) state-of-the-art techniques for removing discrimination from algorithms fail in many situations, at times discriminating against as much as 98% of an input subdomain, (3) Themis optimizations are effective at producing efficient test suites for measuring discrimination, and (4) Themis is more efficient on systems that exhibit more discrimination. We thus demonstrate that fairness testing is a critical aspect of the software development cycle in domains with possible discrimination and provide initial tools for measuring software discrimination. },
	booktitle = {Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering},
	pages = {498–510},
	numpages = {13},
	keywords = {Discrimination testing, software bias, fairness testing, testing},
	location = {Paderborn, Germany},
	series = {ESEC/FSE 2017}
}

@misc{saleiro2018aequitas,
	doi = {10.48550/ARXIV.1811.05577},
	url = {https://arxiv.org/abs/1811.05577}
	author = {Saleiro, Pedro and Kuester, Benedict and Hinkson, Loren and London, Jesse and Stevens, Abby and Anisfeld, Ari and Rodolfa, Kit T. and Ghani, Rayid},
	keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Computers and Society (cs.CY), FOS: Computer and information sciences, FOS: Computer and information sciences},
	title = {Aequitas: A Bias and Fairness Audit Toolkit},
	publisher = {arXiv},
	year = {2018},	
	copyright = {arXiv.org perpetual, non-exclusive license}
}

@INPROCEEDINGS{tramer2017fairtest,
	author={Tramèr, Florian and Atlidakis, Vaggelis and Geambasu, Roxana and Hsu, Daniel and Hubaux, Jean-Pierre and Humbert, Mathias and Juels, Ari and Lin, Huang},
	booktitle={2017 IEEE European Symposium on Security and Privacy (EuroS P)}, 
	title={FairTest: Discovering Unwarranted Associations in Data-Driven Applications}, 
	year={2017},
	volume={},
	number={},
	pages={401-416},
	doi={10.1109/EuroSP.2017.29}}

@phdthesis{adebayo2016fairml,
	title={FairML: ToolBox for diagnosing bias in predictive modeling},
	author={Adebayo, Julius A and others},
	year={2016},
	school={Massachusetts Institute of Technology}
}

@book{hofmann2016rapidminer,
	title={RapidMiner: Data Mining Use Cases and Business Analytics Applications},
	author={Hofmann, M. and Klinkenberg, R.},
	isbn={9781498759861},
	series={Chapman \& Hall/CRC Data Mining and Knowledge Discovery Series},
	url={https://books.google.co.id/books?id=Y\_wYCwAAQBAJ},
	year={2016},
	publisher={CRC Press}
}


@InProceedings{berthold2008knime,
	author="Berthold, Michael R.
	and Cebron, Nicolas
	and Dill, Fabian
	and Gabriel, Thomas R.
	and K{\"o}tter, Tobias
	and Meinl, Thorsten
	and Ohl, Peter
	and Sieb, Christoph
	and Thiel, Kilian
	and Wiswedel, Bernd",
	editor="Preisach, Christine
	and Burkhardt, Hans
	and Schmidt-Thieme, Lars
	and Decker, Reinhold",
	title="KNIME: The Konstanz Information Miner",
	booktitle="Data Analysis, Machine Learning and Applications",
	year="2008",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="319--326",
	abstract="The Konstanz Information Miner is a modular environment, which enables easy visual assembly and interactive execution of a data pipeline. It is designed as a teaching, research and collaboration platform, which enables simple integration of new algorithms and tools as well as data manipulation or visualization methods in the form of new modules or nodes. In this paper we describe some of the design aspects of the underlying architecture and briefly sketch how new nodes can be incorporated.",
	isbn="978-3-540-78246-9"
}

@article{demsar2013orange,
	author  = {Janez Dem\v{s}ar and Toma\v{z} Curk and Ale\v{s} Erjavec and \v{C}rt Gorup and
	Toma\v{z} Ho\v{c}evar and Mitar Milutinovi\v{c} and Martin Mo\v{z}ina and Matija Polajnar and
	Marko Toplak and An\v{z}e Stari\v{c} and Miha \v{S}tajdohar and Lan Umek and
	Lan \v{Z}agar and Jure \v{Z}bontar and Marinka \v{Z}itnik and Bla\v{z} Zupan},
	title   = {Orange: Data Mining Toolbox in Python},
	journal = {Journal of Machine Learning Research},
	year    = {2013},
	volume  = {14},
	pages   = {2349-2353},
	url     = {http://jmlr.org/papers/v14/demsar13a.html}
}

@book{mahoney2020ai,
	title={AI Fairness: How to Measure and Reduce Unwanted Bias in Machine Learning},
	author={Mahoney, T. and Varshney, K.R. and Hind, M. and Safari, an O'Reilly Media Company},
	url={https://books.google.co.id/books?id=uSbfzQEACAAJ},
	year={2020},
	publisher={O'Reilly Media, Incorporated}
}

@book{steinberg2009emf,
	title={EMF: Eclipse Modeling Framework},
	author={Steinberg, D. and Budinsky, F. and Merks, E.},
	isbn={9780321331885},
	lccn={2007049160},
	series={Eclipse (Addison-Wesley)},
	url={https://books.google.co.id/books?id=oAYcAAAACAAJ},
	year={2009},
	publisher={Addison-Wesley}
}

@InProceedings{rose2008egl,
	author="Rose, Louis M.
	and Paige, Richard F.
	and Kolovos, Dimitrios S.
	and Polack, Fiona A. C.",
	editor="Schieferdecker, Ina
	and Hartman, Alan",
	title="The Epsilon Generation Language",
	booktitle="Model Driven Architecture -- Foundations and Applications",
	year="2008",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="1--16",
	abstract="We present the Epsilon Generation Language (EGL), a model-to-text (M2T) transformation language that is a component in a model management tool chain. The distinctive features of EGL are described, in particular its novel design which inherits a number of language concepts and logical features from a base model navigation and modification language. The value of being able to use a M2T language as part of an extensible model management tool chain is outlined in a case study, and EGL is compared to other M2T languages.",
	isbn="978-3-540-69100-6"
}

@inproceedings{dimitris2016flexmi,
	author    = {Dimitrios S. Kolovos and
	Nicholas Matragkas and
	Antonio Garc{\'{\i}}a{-}Dom{\'{\i}}nguez},
	editor    = {Davide Di Ruscio and
	Juan de Lara and
	Alfonso Pierantonio},
	title     = {Towards Flexible Parsing of Structured Textual Model Representations},
	booktitle = {Proceedings of the 2nd Workshop on Flexible Model Driven Engineering
	co-located with {ACM/IEEE} 19th International Conference on Model
	Driven Engineering Languages {\&} Systems (MoDELS 2016), Saint-Malo,
	France, October 2, 2016},
	series    = {{CEUR} Workshop Proceedings},
	volume    = {1694},
	pages     = {22--31},
	publisher = {CEUR-WS.org},
	year      = {2016},
	url       = {http://ceur-ws.org/Vol-1694/FlexMDE2016\_paper\_3.pdf},
	timestamp = {Wed, 12 Feb 2020 16:44:14 +0100},
	biburl    = {https://dblp.org/rec/conf/models/KolovosMG16.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inbook{zucker2020arbiter,
	author = {Zucker, Julian and d'Leeuwen, Myraeka},
	title = {Arbiter: A Domain-Specific Language for Ethical Machine Learning},
	year = {2020},
	isbn = {9781450371100},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/3375627.3375858},
	abstract = {The widespread deployment of machine learning models in high- stakes decision making scenarios requires a code of ethics for machine learning practitioners. We identify four of the primary components required for the ethical practice of machine learn- ing: transparency, fairness, accountability, and reproducibility. We introduce Arbiter, a domain-specific programming language for machine learning practitioners that is designed for ethical machine learning. Arbiter provides a notation for recording how machine learning models will be trained, and we show how this notation can encourage the four described components of ethical machine learning.},
	booktitle = {Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society},
	pages = {421–425},
	numpages = {5}
}

@InProceedings{kamishima2012prejudice,
	author="Kamishima, Toshihiro
	and Akaho, Shotaro
	and Asoh, Hideki
	and Sakuma, Jun",
	editor="Flach, Peter A.
	and De Bie, Tijl
	and Cristianini, Nello",
	title="Fairness-Aware Classifier with Prejudice Remover Regularizer",
	booktitle="Machine Learning and Knowledge Discovery in Databases",
	year="2012",
	publisher="Springer Berlin Heidelberg",
	address="Berlin, Heidelberg",
	pages="35--50",
	abstract="With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives. For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques. Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on. Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination. They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information. In this paper, we first discuss three causes of unfairness in machine learning. We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models. We further apply this approach to logistic regression and empirically show its effectiveness and efficiency.",
	isbn="978-3-642-33486-3"
}


@inproceedings{pleiss2017equal,
	author = {Pleiss, Geoff and Raghavan, Manish and Wu, Felix and Kleinberg, Jon and Weinberger, Kilian Q},
	booktitle = {Advances in Neural Information Processing Systems},
	editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
	pages = {},
	publisher = {Curran Associates, Inc.},
	title = {On Fairness and Calibration},
	url = {https://proceedings.neurips.cc/paper/2017/file/b8b9c74ac526fffbeb2d39ab038d1cd7-Paper.pdf},
	volume = {30},
	year = {2017}
}

@inproceedings{hardt2016equal,
	author = {Hardt, Moritz and Price, Eric and Price, Eric and Srebro, Nati},
	booktitle = {Advances in Neural Information Processing Systems},
	editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
	pages = {},
	publisher = {Curran Associates, Inc.},
	title = {Equality of Opportunity in Supervised Learning},
	url = {https://proceedings.neurips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf},
	volume = {29},
	year = {2016}
}

@INPROCEEDINGS{kamiran2012reject,  author={Kamiran, Faisal and Karim, Asim and Zhang, Xiangliang},  booktitle={2012 IEEE 12th International Conference on Data Mining},   title={Decision Theory for Discrimination-Aware Classification},   year={2012},  volume={},  number={},  pages={924-929},  doi={10.1109/ICDM.2012.45}}

@article{kamiran2011reweighing,
	author    = {Faisal Kamiran and
	Toon Calders},
	title     = {Data preprocessing techniques for classification without discrimination},
	journal   = {Knowl. Inf. Syst.},
	volume    = {33},
	number    = {1},
	pages     = {1--33},
	year      = {2011},
	url       = {https://doi.org/10.1007/s10115-011-0463-8},
	doi       = {10.1007/s10115-011-0463-8},
	timestamp = {Tue, 26 Jun 2018 14:10:08 +0200},
	biburl    = {https://dblp.org/rec/journals/kais/KamiranC11.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{feldman2015disparate,
	author = {Feldman, Michael and Friedler, Sorelle A. and Moeller, John and Scheidegger, Carlos and Venkatasubramanian, Suresh},
	title = {Certifying and Removing Disparate Impact},
	year = {2015},
	isbn = {9781450336642},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/2783258.2783311},
	doi = {10.1145/2783258.2783311},
	abstract = {What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, gender) and an explicit description of the process.When computers are involved, determining disparate impact (and hence bias) is harder. It might not be possible to disclose the process. In addition, even if the process is open, it might be hard to elucidate in a legal setting how the algorithm makes its decisions. Instead of requiring access to the process, we propose making inferences based on the data it uses.We present four contributions. First, we link disparate impact to a measure of classification accuracy that while known, has received relatively little attention. Second, we propose a test for disparate impact based on how well the protected class can be predicted from the other attributes. Third, we describe methods by which data might be made unbiased. Finally, we present empirical evidence supporting the effectiveness of our test for disparate impact and our approach for both masking bias and preserving relevant information in the data. Interestingly, our approach resembles some actual selection practices that have recently received legal scrutiny.},
	booktitle = {Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
	pages = {259–268},
	numpages = {10},
	keywords = {machine learning, fairness, disparate impact},
	location = {Sydney, NSW, Australia},
	series = {KDD '15}
}

@inproceedings{calmon2017optimized,
	author = {Calmon, Flavio and Wei, Dennis and Vinzamuri, Bhanukiran and Natesan Ramamurthy, Karthikeyan and Varshney, Kush R},
	booktitle = {Advances in Neural Information Processing Systems},
	editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
	pages = {},
	publisher = {Curran Associates, Inc.},
	title = {Optimized Pre-Processing for Discrimination Prevention},
	url = {https://proceedings.neurips.cc/paper/2017/file/9a49a25d845a483fae4be7e341368e36-Paper.pdf},
	volume = {30},
	year = {2017}
}

@InProceedings{zemel2013lfr,
	title = 	 {Learning Fair Representations},
	author = 	 {Zemel, Rich and Wu, Yu and Swersky, Kevin and Pitassi, Toni and Dwork, Cynthia},
	booktitle = 	 {Proceedings of the 30th International Conference on Machine Learning},
	pages = 	 {325--333},
	year = 	 {2013},
	editor = 	 {Dasgupta, Sanjoy and McAllester, David},
	volume = 	 {28},
	number =       {3},
	series = 	 {Proceedings of Machine Learning Research},
	address = 	 {Atlanta, Georgia, USA},
	month = 	 {17--19 Jun},
	publisher =    {PMLR},
	pdf = 	 {http://proceedings.mlr.press/v28/zemel13.pdf},
	url = 	 {https://proceedings.mlr.press/v28/zemel13.html},
	abstract = 	 {We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the proportion in the population as a  whole), and individual fairness (similar individuals should be treated similarly).  We formulate fairness as an optimization problem of finding a  good representation of the data with two competing goals: to encode the data as well as possible, while simultaneously obfuscating any information about membership in the protected group.  We show positive results of our algorithm relative to other known techniques, on three datasets.  Moreover, we demonstrate several advantages to our approach.  First, our intermediate representation can be used for other classification tasks (i.e., transfer  learning is possible); secondly, we take a step toward learning a distance metric which can find important dimensions of the data for classification.}
}


@misc{zehlike2017fairness,
	title= {FAIRNESS MEASURES: A Platform for Data Collection and Benchmarking in discrimination-aware ML},
	author= {"Meike Zehlike and Carlos Castillo and Francesco Bonchi and Ricardo Baeza-Yates and Sara Hajian and Mohamed Megahed"},
	howpublished= {\url{https://fairnessmeasures.github.io}},
	month= "Jun",
	year= "2017",
	url="https://fairnessmeasures.github.io"
}

@inproceedings{speicher2018unified,
	author = {Speicher, Till and Heidari, Hoda and Grgic-Hlaca, Nina and Gummadi, Krishna P. and Singla, Adish and Weller, Adrian and Zafar, Muhammad Bilal},
	title = {A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual and Group Unfairness via Inequality Indices},
	year = {2018},
	isbn = {9781450355520},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/3219819.3220046},
	doi = {10.1145/3219819.3220046},
	abstract = {Discrimination via algorithmic decision making has received considerable attention. Prior work largely focuses on defining conditions for fairness, but does not define satisfactory measures of algorithmic unfairness. In this paper, we focus on the following question: Given two unfair algorithms, how should we determine which of the two is more unfair? Our core idea is to use existing inequality indices from economics to measure how unequally the outcomes of an algorithm benefit different individuals or groups in a population. Our work offers a justified and general framework to compare and contrast the (un)fairness of algorithmic predictors. This unifying approach enables us to quantify unfairness both at the individual and the group level. Further, our work reveals overlooked tradeoffs between different fairness notions: using our proposed measures, the overall individual-level unfairness of an algorithm can be decomposed into a between-group and a within-group component. Earlier methods are typically designed to tackle only between-group un- fairness, which may be justified for legal or other reasons. However, we demonstrate that minimizing exclusively the between-group component may, in fact, increase the within-group, and hence the overall unfairness. We characterize and illustrate the tradeoffs between our measures of (un)fairness and the prediction accuracy.},
	booktitle = {Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
	pages = {2239–2248},
	numpages = {10},
	keywords = {fairness in machine learning, inequality indices, fairness measures, generalized entropy, subgroup decomposability, algorithmic decision making, group fairness, individual fairness},
	location = {London, United Kingdom},
	series = {KDD '18}
}

@InProceedings{rogers2015using,
	author="Rogers, Benjamin
	and Qiao, Yechen
	and Gung, James
	and Mathur, Tanmay
	and Burge, Janet E.",
	editor="Gero, John S.
	and Hanna, Sean",
	title="Using Text Mining Techniques to Extract Rationale from Existing Documentation",
	booktitle="Design Computing and Cognition '14",
	year="2015",
	publisher="Springer International Publishing",
	address="Cham",
	pages="457--474",
	abstract="Software development and maintenance require making many decisions over the lifetime of the software. The decision problems, alternative solutions, and the arguments for and against these solutions comprise the system's rationale. This information is potentially valuable as a record of the developer and maintainers' intent. Unfortunately, this information is not explicitly captured in a structured form that can be easily analyzed. Still, while rationale is not explicitly captured, that does not mean that rationale is not captured at all---decisions are documented in many ways throughout the development process. This paper tackles the issue of extracting rationale from text by describing a mechanism for using two existing tools, GATE (General Architecture for Text Engineering) and WEKA (Waikato Environment for Knowledge Analysis) to build classification models for text mining of rationale. We used this mechanism to evaluate different combinations of text features and machine learning algorithms to extract rationale from Chrome bug reports. Our results are comparable in accuracy to those obtained by human annotators.",
	isbn="978-3-319-14956-1"
}

@article{bowen2009document,
	title={Document analysis as a qualitative research method},
	author={Bowen, Glenn A},
	journal={Qualitative research journal},
	year={2009},
	publisher={Emerald Group Publishing Limited},
	url={https://doi.org/10.3316/QRJ0902027}
}


@INPROCEEDINGS{poncin2011process,
	author={Poncin, Wouter and Serebrenik, Alexander and Brand, Mark van den},
	booktitle={2011 15th European Conference on Software Maintenance and Reengineering}, 
	title={Process Mining Software Repositories}, 
	year={2011},
	volume={},
	number={},
	pages={5-14},
	doi={10.1109/CSMR.2011.5}}

@MISC{conceicao2000theyoung,
	author = {Pedro Conceição and Pedro Ferreira},
	title = {1The Young Person’s Guide to the Theil Index: Suggesting Intuitive Interpretations and Exploring Analytical Applications},
	year = {2000}
}

@article{10.2307/2230396,
	author = {Johnston, J.},
	title = "{H. Theil. Economics and Information Theory}",
	journal = {The Economic Journal},
	volume = {79},
	number = {315},
	pages = {601-602},
	year = {1969},
	month = {09},
	issn = {0013-0133},
	doi = {10.2307/2230396},
	url = {https://doi.org/10.2307/2230396},
	eprint = {https://academic.oup.com/ej/article-pdf/79/315/601/27273392/ej0601.pdf},
}


@inproceedings{feldman2015disparate,
	author = {Feldman, Michael and Friedler, Sorelle A. and Moeller, John and Scheidegger, Carlos and Venkatasubramanian, Suresh},
	title = {Certifying and Removing Disparate Impact},
	year = {2015},
	isbn = {9781450336642},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/2783258.2783311},
	doi = {10.1145/2783258.2783311},
	abstract = {What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, gender) and an explicit description of the process.When computers are involved, determining disparate impact (and hence bias) is harder. It might not be possible to disclose the process. In addition, even if the process is open, it might be hard to elucidate in a legal setting how the algorithm makes its decisions. Instead of requiring access to the process, we propose making inferences based on the data it uses.We present four contributions. First, we link disparate impact to a measure of classification accuracy that while known, has received relatively little attention. Second, we propose a test for disparate impact based on how well the protected class can be predicted from the other attributes. Third, we describe methods by which data might be made unbiased. Finally, we present empirical evidence supporting the effectiveness of our test for disparate impact and our approach for both masking bias and preserving relevant information in the data. Interestingly, our approach resembles some actual selection practices that have recently received legal scrutiny.},
	booktitle = {Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
	pages = {259–268},
	numpages = {10},
	keywords = {fairness, machine learning, disparate impact},
	location = {Sydney, NSW, Australia},
	series = {KDD '15}
}

@inproceedings{dwork2012fairness,
	author = {Dwork, Cynthia and Hardt, Moritz and Pitassi, Toniann and Reingold, Omer and Zemel, Richard},
	title = {Fairness through Awareness},
	year = {2012},
	isbn = {9781450311151},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/2090236.2090255},
	doi = {10.1145/2090236.2090255},
	abstract = {We study fairness in classification, where individuals are classified, e.g., admitted to a university, and the goal is to prevent discrimination against individuals based on their membership in some group, while maintaining utility for the classifier (the university). The main conceptual contribution of this paper is a framework for fair classification comprising (1) a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand; (2) an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly. We also present an adaptation of our approach to achieve the complementary goal of "fair affirmative action," which guarantees statistical parity (i.e., the demographics of the set of individuals receiving any classification are the same as the demographics of the underlying population), while treating similar individuals as similarly as possible. Finally, we discuss the relationship of fairness to privacy: when fairness implies privacy, and how tools developed in the context of differential privacy may be applied to fairness.},
	booktitle = {Proceedings of the 3rd Innovations in Theoretical Computer Science Conference},
	pages = {214–226},
	numpages = {13},
	location = {Cambridge, Massachusetts},
	series = {ITCS '12}
}

@book{volter2013model,
	title={Model-Driven Software Development: Technology, Engineering, Management},
	author={V{\"o}lter, M. and Stahl, T. and Bettin, J. and Haase, A. and Helsen, S. and Czarnecki, K. and von Stockfleth, B.},
	isbn={9781118725764},
	lccn={2006007375},
	series={Wiley Software Patterns Series},
	url={https://books.google.co.uk/books?id=9ww\_D9fAKncC},
	year={2013},
	publisher={Wiley}
}

@book{brambilla2017model,
	title={Model-Driven Software Engineering in Practice},
	author={Brambilla, M. and Cabot, J. and Wimmer, M.},
	isbn={9781627057080},
	series={Synthesis Lectures on Software Engineering},
	url={https://books.google.co.uk/books?id=dHUuswEACAAJ},
	year={2017},
	publisher={Morgan \& Claypool Publishers}
}

@book{muller2016introduction,
	title={Introduction to Machine Learning with Python: A Guide for Data Scientists},
	author={M{\"u}ller, A.C. and Guido, S.},
	isbn={9781449369897},
	url={https://books.google.co.uk/books?id=vbQlDQAAQBAJ},
	year={2016},
	publisher={O'Reilly Media}
}

@book{byrne2017development,
	title={Development Workflows for Data Scientists},
	author={Byrne, C.},
	url={https://books.google.co.uk/books?id=84HgwQEACAAJ},
	year={2017},
	publisher={O'Reilly Media}
}

@inbook{lee2021landscape,
	author = {Lee, Michelle Seng Ah and Singh, Jat},
	title = {The Landscape and Gaps in Open Source Fairness Toolkits},
	year = {2021},
	isbn = {9781450380966},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/3411764.3445261},
	abstract = { With the surge in literature focusing on the assessment and mitigation of unfair outcomes in algorithms, several open source ‘fairness toolkits’ recently emerged to make such methods widely accessible. However, little studied are the differences in approach and capabilities of existing fairness toolkits, and their fit-for-purpose in commercial contexts. Towards this, this paper identifies the gaps between the existing open source fairness toolkit capabilities and the industry practitioners’ needs. Specifically, we undertake a comparative assessment of the strengths and weaknesses of six prominent open source fairness toolkits, and investigate the current landscape and gaps in fairness toolkits through an exploratory focus group, a semi-structured interview, and an anonymous survey of data science/machine learning (ML) practitioners. We identify several gaps between the toolkits’ capabilities and practitioner needs, highlighting areas requiring attention and future directions towards tooling that better support ‘fairness in practice.’},
	booktitle = {Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems},
	articleno = {699},
	numpages = {13}
}

@ARTICLE{googlewhatif2020,
	author={Wexler, James and Pushkarna, Mahima and Bolukbasi, Tolga and Wattenberg, Martin and Viégas, Fernanda and Wilson, Jimbo},
	journal={IEEE Transactions on Visualization and Computer Graphics}, 
	title={The What-If Tool: Interactive Probing of Machine Learning Models}, 
	year={2020},
	volume={26},
	number={1},
	pages={56-65},
	doi={10.1109/TVCG.2019.2934619}}


@misc{saleiro2019aequitas,
	title={Aequitas: A Bias and Fairness Audit Toolkit}, 
	author={Pedro Saleiro and Benedict Kuester and Loren Hinkson and Jesse London and Abby Stevens and Ari Anisfeld and Kit T. Rodolfa and Rayid Ghani},
	year={2019},
	eprint={1811.05577},
	archivePrefix={arXiv},
	url={https://arxiv.org/abs/1811.05577},
	primaryClass={cs.LG}
}

@misc{aequitas2022,
	title={Aequitas},
	author={{Aequitas}},
	year={2022},
	url={http://www.datasciencepublicpolicy.org/our-work/tools-guides/aequitas/},
	note = {Accessed: 2022-01-30}
}


@misc{fairlearn2022,
	title={Improve fairness of AI systems},
	author={Fairlearn},
	year={2022},
	url={https://fairlearn.org/},
	note = {Accessed: 2022-01-30}
}

@misc{scikitlego2022,
	title={scikit-lego},
	author={{scikit-lego}},
	year={2022},
	url={https://scikit-lego.readthedocs.io/en/latest/index.html},
	note = {Accessed: 2022-01-30}
}

@misc{scikitfairness2022,
	title={scikit-fairness},
	author={{scikit-fairness}},
	year={2022},
	url={https://scikit-fairness.netlify.app/},
	note = {Accessed: 2022-01-30}
}

@techreport{bird2020fairlearn,
	author = {Bird, Sarah and Dud{\'i}k, Miro and Edgar, Richard and Horn, Brandon and Lutz, Roman and Milan, Vanessa and Sameki, Mehrnoosh and Wallach, Hanna and Walker, Kathleen},
	title = {Fairlearn: A toolkit for assessing and improving fairness in {AI}},
	institution = {Microsoft},
	year = {2020},
	month = {May},
	url = "https://www.microsoft.com/en-us/research/publication/fairlearn-a-toolkit-for-assessing-and-improving-fairness-in-ai/",
	number = {MSR-TR-2020-32},
}

@misc{ibmaif3602022guidance,
	title={Guidance on choosing metrics and mitigation},
	author={{IBM Research Trusted AI}},
	year={2022},
	url={https://aif360.mybluemix.net/resources#guidance},
	note = {Accessed: 2022-01-30}
}

@misc{ibmaif3602022doc,
	title={AI Fairness 360 documentation},
	author={{AI Fairness 360 (AIF360) Authors}},
	year={2022},
	url={https://aif360.readthedocs.io/en/stable/},
	note = {Accessed: 2022-01-30}
}

@misc{lale2022doc,
	title={Welcome to LALE’s API documentation!},
	author={{IBM AI Research}},
	year={2022},
	url={https://lale.readthedocs.io/en/latest/modules/lale.lib.aif360.util.html#lale.lib.aif360.util.theil_index},
	note = {Accessed: 2022-01-30}
}

@misc{evans2017yaml,
	title={YAML Ain’t Markup Language (YAML™) Version 1.2.},
	author={Evans, Clark and Ben-Kiki, O and d{\"o}t Net, I},
	year={2017},
	url={https://yaml.org/spec/1.2.2},
	note = {Accessed: 2022-01-19}
}

@misc{angwin2016machine,
	title={Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica (2016)},
	author={Angwin, Julia and Larson, Jeff and Mattu, Surya and Kirchner, Lauren},
	journal={Google Scholar},
	pages={23},
	year={2016},
	url={https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing},
	note = {Accessed: 2022-01-18}
}

@InProceedings{buolamwini2018gender,
	title = 	 {Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification},
	author = 	 {Buolamwini, Joy and Gebru, Timnit},
	booktitle = 	 {Proceedings of the 1st Conference on Fairness, Accountability and Transparency},
	pages = 	 {77--91},
	year = 	 {2018},
	editor = 	 {Friedler, Sorelle A. and Wilson, Christo},
	volume = 	 {81},
	series = 	 {Proceedings of Machine Learning Research},
	month = 	 {23--24 Feb},
	publisher =    {PMLR},
	pdf = 	 {http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf},
	url = 	 {https://proceedings.mlr.press/v81/buolamwini18a.html},
	abstract = 	 {Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. In this work, we present an approach to evaluate bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Using the dermatologist  approved Fitzpatrick Skin Type classification system, we characterize the gender and skin type distribution of two facial analysis benchmarks, IJB-A and Adience. We find that these datasets are overwhelmingly composed of lighter-skinned subjects (79.6% for IJB-A and 86.2% for Adience) and introduce a new facial analysis dataset which is balanced by gender and skin type. We evaluate 3 commercial gender classification systems using our dataset and show that darker-skinned females are the most misclassified group (with error rates of up to 34.7%). The maximum error rate for lighter-skinned males is 0.8%. The substantial disparities in the accuracy of classifying darker females, lighter females, darker males, and lighter males in gender classification systems require urgent attention if commercial companies are to build genuinely fair, transparent and accountable facial analysis algorithms.}
}

@INPROCEEDINGS{lahoti2019ifair,
	author={Lahoti, Preethi and Gummadi, Krishna P. and Weikum, Gerhard},
	booktitle={2019 IEEE 35th International Conference on Data Engineering (ICDE)}, 
	title={iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making}, 
	year={2019},
	volume={},
	number={},
	pages={1334-1345},
	doi={10.1109/ICDE.2019.00121}}

@inproceedings{chen2019fairness,
	author = {Chen, Jiahao and Kallus, Nathan and Mao, Xiaojie and Svacha, Geoffry and Udell, Madeleine},
	title = {Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved},
	year = {2019},
	isbn = {9781450361255},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/3287560.3287594},
	doi = {10.1145/3287560.3287594},
	abstract = {Assessing the fairness of a decision making system with respect to a protected class, such as gender or race, is challenging when class membership labels are unavailable. Probabilistic models for predicting the protected class based on observable proxies, such as surname and geolocation for race, are sometimes used to impute these missing labels for compliance assessments. Empirically, these methods are observed to exaggerate disparities, but the reason why is unknown. In this paper, we decompose the biases in estimating outcome disparity via threshold-based imputation into multiple interpretable bias sources, allowing us to explain when over- or underestimation occurs. We also propose an alternative weighted estimator that uses soft classification, and show that its bias arises simply from the conditional covariance of the outcome with the true class membership. Finally, we illustrate our results with numerical simulations and a public dataset of mortgage applications, using geolocation as a proxy for race. We confirm that the bias of threshold-based imputation is generally upward, but its magnitude varies strongly with the threshold chosen. Our new weighted estimator tends to have a negative bias that is much simpler to analyze and reason about.},
	booktitle = {Proceedings of the Conference on Fairness, Accountability, and Transparency},
	pages = {339–348},
	numpages = {10},
	keywords = {protected class, race imputation, racial discrimination, fair lending, Bayesian Improved Surname Geocoding, probablistic proxy model, disparate impact},
	location = {Atlanta, GA, USA},
	series = {FAT* '19}
}

 @misc{oxford2022bias,
 	author={{Oxford Reference}},
 	title = {Bias},
 	year = {2022},
 	url={https://www.oxfordreference.com/view/10.1093/oi/authority.20110803095504939},
 	note = {Accessed: 2022-01-16}
 }
 
@misc{bellamy2018ai,
	title={AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias}, 
	author={Rachel K. E. Bellamy and Kuntal Dey and Michael Hind and Samuel C. Hoffman and Stephanie Houde and Kalapriya Kannan and Pranay Lohia and Jacquelyn Martino and Sameep Mehta and Aleksandra Mojsilovic and Seema Nagar and Karthikeyan Natesan Ramamurthy and John Richards and Diptikalyan Saha and Prasanna Sattigeri and Moninder Singh and Kush R. Varshney and Yunfeng Zhang},
	year={2018},
	eprint={1810.01943},
	archivePrefix={arXiv},
	url={https://arxiv.org/abs/1810.01943},
	primaryClass={cs.AI}
}

@article{mehrabi2021survey,
	author = {Mehrabi, Ninareh and Morstatter, Fred and Saxena, Nripsuta and Lerman, Kristina and Galstyan, Aram},
	title = {A Survey on Bias and Fairness in Machine Learning},
	year = {2021},
	issue_date = {July 2022},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	volume = {54},
	number = {6},
	issn = {0360-0300},
	url = {https://doi.org/10.1145/3457607},
	doi = {10.1145/3457607},
	abstract = {With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.},
	journal = {ACM Comput. Surv.},
	month = {jul},
	articleno = {115},
	numpages = {35},
	keywords = {machine learning, representation learning, deep learning, natural language processing, Fairness and bias in artificial intelligence}
}