@inproceedings{Representation_bias, author = {Suresh, Harini and Guttag, John}, title = {A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle}, year = {2021}, isbn = {9781450385534}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3465416.3483305}, doi = {10.1145/3465416.3483305}, abstract = {As machine learning (ML) increasingly affects people and society, awareness of its potential unwanted consequences has also grown. To anticipate, prevent, and mitigate undesirable downstream consequences, it is critical that we understand when and how harm might be introduced throughout the ML life cycle. In this paper, we provide a framework that identifies seven distinct potential sources of downstream harm in machine learning, spanning data collection, development, and deployment. In doing so, we aim to facilitate more productive and precise communication around these issues, as well as more direct, application-grounded ways to mitigate them.}, booktitle = {Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization}, articleno = {17}, numpages = {9}, keywords = {AI ethics, algorithmic bias, allocative harm, fairness in machine learning, representational harm, societal implications of machine learning}, location = {--, NY, USA}, series = {EAAMO '21} }

@misc{COMPAS_score,
  author       = {Julia Angwin and Jeff Larson and Surya Mattu and Lauren Kirchner},
  title        = {Machine Bias: There's Software Used Across the Country to Predict Future Criminals. And it's Biased Against Blacks},
  year         = {2016},
  url          = {https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing},
  note         = {Accessed: 2024-11-09},
  publisher    = {ProPublica}
}


@misc{Recruitment_case ,
  author = {Dave Gershgorn},
  title = {Amazon reportedly scraps internal AI recruiting tool that was biased against women},
  year = {2018},
  url = {https://www.theverge.com/2018/10/10/17958784/ai-recruiting-tool-bias-amazon-report},
  note = {Accessed: 2024-11-3}
}
@misc{Longo2025,
  author = {Maria Antonietta Longo},
  title = {Official code repository},
  howpublished = {[Online]},
  url = {https://github.com/MariaAntoniettaL/Synthetic-Data-Generation-Bias-Mitigation-Subgroup-Based-for-Structured-Data},
note = {https://github.com/MariaAntoniettaL/Synthetic-Data-Generation-Bias-Mitigation-Subgroup-Based-for-Structured-Data},
}

@misc{UK_grades_bias,
  author = {James Vincent},
  title = {UK A-level Results Algorithm Was Biased Against Disadvantaged Students, Report Finds},
  year = {2020},
  url = {https://www.theverge.com/2020/8/17/21372045/uk-a-level-results-algorithm-biased-coronavirus-covid-19-pandemic-university-applications},
  note = {Accessed: 2024-11-09},
  # howpublished = {\url{https://www.theverge.com/2020/8/17/21372045/uk-a-level-results-algorithm-biased-coronavirus-covid-19-pandemic-university-applications}},
}



@inproceedings{divexplorer,
author = {Pastor, Eliana and de Alfaro, Luca and Baralis, Elena},
title = {Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence},
year = {2021},
isbn = {9781450383431},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3448016.3457284},
doi = {10.1145/3448016.3457284},
abstract = {Machine learning models may perform differently on different data subgroups, which we represent as itemsets (i.e., conjunctions of simple predicates). The identification of these critical data subgroups plays an important role in many applications, for example model validation and testing, or evaluation of model fairness. Typically, domain expert help is required to identify relevant (or sensitive) subgroups.We propose the notion of divergence over itemsets as a measure of different classification behavior on data subgroups, and the use of frequent pattern mining techniques for their identification. A quantification of the contribution of different attribute values to divergence, based on the mathematical foundations provided by Shapley values, allows us to identify both critical and peculiar behaviors of attributes. Extended experiments show the effectiveness of the approach in identifying critical subgroup behaviors.},
booktitle = {Proceedings of the 2021 International Conference on Management of Data},
pages = {1400–1412},
numpages = {13},
keywords = {shapley value, machine-learning model debugging, fairness in machine learning, classifier validation, bias detection},
location = {Virtual Event, China},
series = {SIGMOD '21}
}

@inproceedings{divexplorer,
author = {Pastor, Eliana and de Alfaro, Luca and Baralis, Elena},
title = {Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence},
year = {2021},
isbn = {9781450383431},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3448016.3457284},
doi = {10.1145/3448016.3457284},
abstract = {Machine learning models may perform differently on different data subgroups, which we represent as itemsets (i.e., conjunctions of simple predicates). The identification of these critical data subgroups plays an important role in many applications, for example model validation and testing, or evaluation of model fairness. Typically, domain expert help is required to identify relevant (or sensitive) subgroups.We propose the notion of divergence over itemsets as a measure of different classification behavior on data subgroups, and the use of frequent pattern mining techniques for their identification. A quantification of the contribution of different attribute values to divergence, based on the mathematical foundations provided by Shapley values, allows us to identify both critical and peculiar behaviors of attributes. Extended experiments show the effectiveness of the approach in identifying critical subgroup behaviors.},
booktitle = {Proceedings of the 2021 International Conference on Management of Data},
pages = {1400–1412},
numpages = {13},
keywords = {bias detection, classifier validation, fairness in machine learning, machine-learning model debugging, shapley value},
location = {Virtual Event, China},
series = {SIGMOD '21}
}

@INPROCEEDINGS{speech,
  author={Koudounas, Alkis and Pastor, Eliana and Attanasio, Giuseppe and de Alfaro, Luca and Baralis, Elena},
  booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Prioritizing Data Acquisition for end-to-end Speech Model Improvement}, 
  year={2024},
  volume={},
  number={},
  pages={7000-7004},
  keywords={Training;Costs;Intent recognition;Data acquisition;Signal processing;Data models;Object recognition;spoken language understanding;data acquisition;data markets;divergence},
  doi={10.1109/ICASSP48485.2024.10446326}}



@misc{sottogruppi_per_immagini,
      title={Discover and Mitigate Multiple Biased Subgroups in Image Classifiers}, 
      author={Zeliang Zhang and Mingqian Feng and Zhiheng Li and Chenliang Xu},
      year={2024},
      eprint={2403.12777},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2403.12777}, 
}


@misc{immagini_no_sottogruppi,
      title={Partition-and-Debias: Agnostic Biases Mitigation via A Mixture of Biases-Specific Experts}, 
      author={Jiaxuan Li and Duc Minh Vo and Hideki Nakayama},
      year={2023},
      eprint={2308.10005},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2308.10005}, 
}


@inbook{fairdo,
   title={Trusting Fair Data: Leveraging Quality in Fairness-Driven Data Removal  echniques},
   ISBN={9783031683237},
   ISSN={1611-3349},
   url={http://dx.doi.org/10.1007/978-3-031-68323-7_33},
   DOI={10.1007/978-3-031-68323-7_33},
   booktitle={Big Data Analytics and Knowledge Discovery},
   publisher={Springer Nature Switzerland},
   author={Duong, Manh Khoi and Conrad, Stefan},
   year={2024},
   pages={375–380} }


@book{gal,
	title={Nuovi studii sugli astri medicei}, 
	publisher={Manuzio},
	author={G.~Galilei},
	year={1612}
}

@book{tor1,
	title={La pressione barometrica},
	publisher={Il Porcellino},
	author={E.~Torricelli},
	year={1606}
} 

@article{tor2,
	title={Delle misure},
	author={E.~Torricelli and  A.~Vasari},
	journal={Atti Nuovo Cimento},
	volume={III},
	number={2},
	pages={27--31},
	year={1607}
} 
@book{gasparini,
  author    = {Mauro Gasparini},
  title     = {Modelli Probabilistici e Statistici},
  year      = {2009},
  publisher = {Pearson},
  address   = {Milan, Italy}
}


@misc{bayes,
  author       = {Will Kurt},
  title        = {A Gentle Introduction to Bayesian Statistics},
  year         = {2015},
  url          = {https://www.countbayesie.com/blog/2015/2/18/bayes-theorem-for-humans},
  note         = {Accessed: January 7, 2025}
}


@misc{compare_generativa,
      title={Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study}, 
      author={Emmanouil Panagiotou and Arjun Roy and Eirini Ntoutsi},
      year={2024},
      eprint={2409.05215},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2409.05215}, 
}
@article{duane1964,
	title={Learning Curve Approach To Reliability Monitoring},
	author={Duane J.T.},
	journal={IEEE Transactions on Aerospace},
	volume={2},
	pages={563-566},
	year={1964}
} 

@book{bear,
  title={Dynamics of fluids in porous media},
  publisher={American Elseuier},
  author={Bear, Jacob},
  year={1972}
}

@book{dimo,
  title={ingegneria degli acquiferi},
  author={Di Molfetta, Antonio and Sethi, Rajandrea},
  publisher={Springer},
  year={2012}
}

@book{ferziger,
  title={Computational methods for fluid mechanics},
  author={Ferziger, Joel H and Peric, Milovan},
  publisher={Springer},
  year={2002}
}

@book{bird,
  title={Transport phenomena},
  author={Bird, R Byron and Stewart, Warren E and Lightfoot, Edwin N},
  year={2007},
  publisher={Wiley. com}
}


@article{smote,
  author       = {Chawla, Nitesh V. and Bowyer, Kevin W. and Hall, Lawrence O. and Kegelmeyer, W. Philip},
  title        = {SMOTE: Synthetic Minority Over-sampling Technique},
  journal      = {Journal of Artificial Intelligence Research (JAIR)},
  volume       = {16},
  pages        = {321--357},
  year         = {2002},
  doi          = {10.1613/jair.953},
  abstract     = {An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples...}
}


@article{tizi,
  title={Transport of non-Newtonian suspensions of highly concentrated micro-and nanoscale iron particles in porous media: a modeling approach},
  author={Tosco, Tiziana and Sethi, Rajandrea},
  journal={Environmental science \& technology},
  volume={44},
  number={23},
  pages={9062--9068},
  year={2010},
  publisher={ACS Publications}
}

@misc{matteo,
	author={Icardi, Matteo and Boccardo, Gianluca and Tosco, Tiziana and Marchisio, Daniele l. and Sethi, Rajandrea},
	title={Pore-scale simulation of fluid flow and solute dispersion in three-dimensional porous media},
	note={(in preparation)}
}

@misc{dimarco,
	author={Di Marco, P},
	title={Lo strato limite laminare},
	note={Termofluidodinamica Appl. - lecture notes}
}

@article{gianluca,
  title={Microscale simulation of particle deposition in porous media},
  author={Marchisio, Daniele and Sethi, Rajandrea and Boccardo, Gianluca},
  journal={JOURNAL OF COLLOID AND INTERFACE SCIENCE},
  publisher={Academic Press}
}

@article{tritton,
  title={Physical fluid dynamics},
  author={Tritton, David J},
  journal={Oxford, Clarendon Press, 1988, 536 p.},
  volume={1},
  year={1988}
}

@misc{matlab,
      author = "MathWorks",
      title = "MATLAB Documentation Center",
      url = "http://www.mathworks.it/it/help/matlab/"
}

@article{battiato1,
  title={Hybrid models of reactive transport in porous and fractured media},
  author={Battiato, Ilenia and Tartakovsky, Daniel M and Tartakovsky, Alexandre M and Scheibe, Timothy D},
  journal={Advances in Water Resources},
  volume={34},
  number={9},
  pages={1140--1150},
  year={2011},
  publisher={Elsevier}
}

@article{mikelic,
  title={Rigorous upscaling of the reactive flow through a pore, under dominant Peclet and Damkohler numbers},
  author={Mikelic, Andro and Devigne, Vincent and Van Duijn, CJ},
  journal={SIAM journal on mathematical analysis},
  volume={38},
  number={4},
  pages={1262--1287},
  year={2006},
  publisher={SIAM}
}

@article{battiato2,
  title={Applicability regimes for macroscopic models of reactive transport in porous media},
  author={Battiato, I and Tartakovsky, DM},
  journal={Journal of contaminant hydrology},
  volume={120},
  pages={18--26},
  year={2011},
  publisher={Elsevier}
}

@article{tufenkji2004correlation,
  title={Correlation equation for predicting single-collector efficiency in physicochemical filtration in saturated porous media},
  author={Tufenkji, Nathalie and Elimelech, Menachem},
  journal={Environmental science \& technology},
  volume={38},
  number={2},
  pages={529--536},
  year={2004},
  publisher={ACS Publications}
}

@article{yao1971water,
  title={Water and waste water filtration. Concepts and applications},
  author={Yao, Kuan-Mu and Habibian, Mohammad T and O'Melia, Charles R},
  journal={Environmental Science \& Technology},
  volume={5},
  number={11},
  pages={1105--1112},
  year={1971},
  publisher={ACS Publications}
}

@misc{historical_bias,
      title={A Survey on Bias and Fairness in Machine Learning}, 
      author={Ninareh Mehrabi and Fred Morstatter and Nripsuta Saxena and Kristina Lerman and Aram Galstyan},
      year={2022},
      eprint={1908.09635},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1908.09635}, 
}

@online{fortune,
  author    = {Fortune},
  title     = {Fortune 500 companies with women CEOs in 2024},
  year      = {2024},
  url       = {https://fortune.com/2024/06/04/fortune-500-companies-women-ceos-2024/?utm_source=chatgpt.com},
  note      = {Accessed: 2024-12-24}
}

@article{survey,
author = {Shahbazi, Nima and Lin, Yin and Asudeh, Abolfazl and Jagadish, H. V.},
title = {Representation Bias in Data: A Survey on Identification and Resolution Techniques},
year = {2023},
issue_date = {December 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {55},
number = {13s},
issn = {0360-0300},
url = {https://doi.org/10.1145/3588433},
doi = {10.1145/3588433},
abstract = {Data-driven algorithms are only as good as the data they work with, while datasets, especially social data, often fail to represent minorities adequately. Representation Bias in data can happen due to various reasons, ranging from historical discrimination to selection and sampling biases in the data acquisition and preparation methods. Given that “bias in, bias out,” one cannot expect AI-based solutions to have equitable outcomes for societal applications, without addressing issues such as representation bias. While there has been extensive study of fairness in machine learning models, including several review papers, bias in the data has been less studied. This article reviews the literature on identifying and resolving representation bias as a feature of a dataset, independent of how consumed later. The scope of this survey is bounded to structured (tabular) and unstructured (e.g., image, text, graph) data. It presents taxonomies to categorize the studied techniques based on multiple design dimensions and provides a side-by-side comparison of their properties.There is still a long way to fully address representation bias issues in data. The authors hope that this survey motivates researchers to approach these challenges in the future by observing existing work within their respective domains.},
journal = {ACM Comput. Surv.},
month = jul,
articleno = {293},
numpages = {39},
keywords = {Responsible data science, fairness in machine learning, data equity systems, data-centric AI, AI-ready data}
}

@misc{us_census_2019,
  author    = {US Census Bureau},
  title     = {The Asian and Pacific Islander Population in the United States: May 2019},
  year      = {2019},
  url       = {https://www.google.com/search?client=safari&rls=en&q=2019.+The+Asian+and+Pacific+Islander+Population+in+the+United+States%3A+May+2019.+US+Census+Bureau.&ie=UTF-8&oe=UTF-8},
  note      = {Accessed: 2024-12-24}
}
