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@article{van2008visualizing,
  title={Visualizing data using t-SNE.},
  author={Van der Maaten, Laurens and Hinton, Geoffrey},
  journal={Journal of machine learning research},
  volume={9},
  number={11},
  year={2008}
}

@techreport{doudchenko2016balancing,
  title={Balancing, regression, difference-in-differences and synthetic control methods: A synthesis},
  author={Doudchenko, Nikolay and Imbens, Guido W},
  year={2016},
  institution={National Bureau of Economic Research}
}

@article{abadie2015comparative,
  title={Comparative politics and the synthetic control method},
  author={Abadie, Alberto and Diamond, Alexis and Hainmueller, Jens},
  journal={American Journal of Political Science},
  volume={59},
  number={2},
  pages={495--510},
  year={2015},
  publisher={Wiley Online Library}
}

@article{abadie2010synthetic,
  title={Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program},
  author={Abadie, Alberto and Diamond, Alexis and Hainmueller, Jens},
  journal={Journal of the American statistical Association},
  volume={105},
  number={490},
  pages={493--505},
  year={2010},
  publisher={Taylor \& Francis}
}

@article{serra2022reforma,
  title={A reforma trabalhista de 2017 teve efeito sobre a taxa de desemprego no Brasil? Uma an{\'a}lise dos primeiros anos de vig{\^e}ncia da Lei 13.467/2017},
  author={Serra, Gustavo Pereira and Bottega, Ana and Sanches, Marina Da Silva},
  journal={Nota de Pol{\'\i}tica Econ{\^o}mica},
  volume={21},
  year={2022}
}

@inproceedings{ester1996density,
  title={A density-based algorithm for discovering clusters in large spatial databases with noise},
  author={Ester, Martin and Kriegel, Hans-Peter and Sander, J{\"o}rg and Xu, Xiaowei and others},
  booktitle={Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining},
  volume={96},
  number={34},
  pages={226--231},
  year={1996}
}

@article{frey2007clustering,
  title={Clustering by passing messages between data points},
  author={Frey, Brendan J and Dueck, Delbert},
  journal={science},
  volume={315},
  number={5814},
  pages={972--976},
  year={2007},
  publisher={American Association for the Advancement of Science}
}

@techreport{gingado,
    author = {Araujo, Douglas KG},
    title = {gingado: a machine learning library focused on economics and finance},
    series = {BIS Working Paper},
    type = {Working Paper},
    institution = {Bank for International Settlements},
    year = {2023},
    number = {1122}
}

@article{abadie2011synth,
  title={Synth: An R package for synthetic control methods in comparative case studies},
  author={Abadie, Alberto and Diamond, Alexis and Hainmueller, Jens},
  journal={Journal of Statistical Software},
  volume={42},
  number={13},
  year={2011}
}

@article{abadie2021using,
  title={Using synthetic controls: Feasibility, data requirements, and methodological aspects},
  author={Abadie, Alberto},
  journal={Journal of Economic Literature},
  volume={59},
  number={2},
  pages={391--425},
  year={2021}
}

@article{abadie2003economic,
  title={The economic costs of conflict: A case study of the Basque Country},
  author={Abadie, Alberto and Gardeazabal, Javier},
  journal={American economic review},
  volume={93},
  number={1},
  pages={113--132},
  year={2003}
}

@article{ModelCards,
  author    = {Margaret Mitchell and
               Simone Wu and
               Andrew Zaldivar and
               Parker Barnes and
               Lucy Vasserman and
               Ben Hutchinson and
               Elena Spitzer and
               Inioluwa Deborah Raji and
               Timnit Gebru},
  title     = {Model Cards for Model Reporting},
  journal   = {CoRR},
  volume    = {abs/1810.03993},
  year      = {2018},
  url       = {http://arxiv.org/abs/1810.03993},
  eprinttype = {arXiv},
  eprint    = {1810.03993},
  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-03993.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{babenko2021classical,
  title={Classical machine learning methods in economics research: Macro and micro level example},
  author={Babenko, VITALINA and Panchyshyn, ANDRIY and Zomchak, LARYSA and Nehrey, MARYNA and Artym-Drohomyretska, ZORIANA and Lahotskyi, TARAS},
  journal={WSEAS Transactions on Business and Economics},
  volume={18},
  pages={209--217},
  year={2021}
}

@article{gogas2021machine,
  title={Machine learning in economics and finance},
  author={Gogas, Periklis and Papadimitriou, Theophilos},
  journal={Computational Economics},
  volume={57},
  number={1},
  pages={1--4},
  year={2021},
  publisher={Springer}
}

@techreport{chernozhukov2018generic,
  title={Generic machine learning inference on heterogeneous treatment effects in randomized experiments, with an application to immunization in India},
  author={Chernozhukov, Victor and Demirer, Mert and Duflo, Esther and Fernandez-Val, Ivan},
  year={2018},
  institution={National Bureau of Economic Research}
}

@article{PredictionProblems,
Author = {Kleinberg, Jon and Ludwig, Jens and Mullainathan, Sendhil and Obermeyer, Ziad},
Title = {Prediction Policy Problems},
Journal = {American Economic Review},
Volume = {105},
Number = {5},
Year = {2015},
Month = {May},
Pages = {491-95},
DOI = {10.1257/aer.p20151023},
URL = {https://www.aeaweb.org/articles?id=10.1257/aer.p20151023}}

@article{thomas2022reliance,
  title={Reliance on metrics is a fundamental challenge for AI},
  author={Thomas, Rachel L and Uminsky, David},
  journal={Patterns},
  volume={3},
  number={5},
  pages={100476},
  year={2022},
  publisher={Elsevier}
}
@article{cybenko1989approximation,
  title={Approximation by superpositions of a sigmoidal function},
  author={Cybenko, George},
  journal={Mathematics of control, signals and systems},
  volume={2},
  number={4},
  pages={303--314},
  year={1989},
  publisher={Springer}
}

@Article{fastaiAPI,
AUTHOR = {Howard, Jeremy and Gugger, Sylvain},
TITLE = {Fastai: A Layered API for Deep Learning},
JOURNAL = {Information},
VOLUME = {11},
YEAR = {2020},
NUMBER = {2},
ARTICLE-NUMBER = {108},
URL = {https://www.mdpi.com/2078-2489/11/2/108},
ISSN = {2078-2489},
ABSTRACT = {fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai includes: a new type dispatch system for Python along with a semantic type hierarchy for tensors; a GPU-optimized computer vision library which can be extended in pure Python; an optimizer which refactors out the common functionality of modern optimizers into two basic pieces, allowing optimization algorithms to be implemented in 4–5 lines of code; a novel 2-way callback system that can access any part of the data, model, or optimizer and change it at any point during training; a new data block API; and much more. We used this library to successfully create a complete deep learning course, which we were able to write more quickly than using previous approaches, and the code was more clear. The library is already in wide use in research, industry, and teaching.},
DOI = {10.3390/info11020108}
}




@article{automateMH,
Author = {Mullainathan, Sendhil and Obermeyer, Ziad},
Title = {Does Machine Learning Automate Moral Hazard and Error?},
Journal = {American Economic Review},
Volume = {107},
Number = {5},
Year = {2017},
Month = {May},
Pages = {476-80},
DOI = {10.1257/aer.p20171084},
URL = {https://www.aeaweb.org/articles?id=10.1257/aer.p20171084}}

@article{sklearnAPI,
  author    = {Lars Buitinck and
               Gilles Louppe and
               Mathieu Blondel and
               Fabian Pedregosa and
               Andreas Mueller and
               Olivier Grisel and
               Vlad Niculae and
               Peter Prettenhofer and
               Alexandre Gramfort and
               Jaques Grobler and
               Robert Layton and
               Jake VanderPlas and
               Arnaud Joly and
               Brian Holt and
               Ga{\"{e}}l Varoquaux},
  title     = {{API} design for machine learning software: experiences from the scikit-learn
               project},
  journal   = {CoRR},
  volume    = {abs/1309.0238},
  year      = {2013},
  url       = {http://arxiv.org/abs/1309.0238},
  eprinttype = {arXiv},
  eprint    = {1309.0238},
  timestamp = {Mon, 13 Aug 2018 16:49:16 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/BuitinckLBPMGNPGGLVJHV13.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@Article{fastai,
AUTHOR = {Howard, Jeremy and Gugger, Sylvain},
TITLE = {Fastai: A Layered API for Deep Learning},
JOURNAL = {Information},
VOLUME = {11},
YEAR = {2020},
NUMBER = {2},
ARTICLE-NUMBER = {108},
URL = {https://www.mdpi.com/2078-2489/11/2/108},
ISSN = {2078-2489},
ABSTRACT = {fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai includes: a new type dispatch system for Python along with a semantic type hierarchy for tensors; a GPU-optimized computer vision library which can be extended in pure Python; an optimizer which refactors out the common functionality of modern optimizers into two basic pieces, allowing optimization algorithms to be implemented in 4–5 lines of code; a novel 2-way callback system that can access any part of the data, model, or optimizer and change it at any point during training; a new data block API; and much more. We used this library to successfully create a complete deep learning course, which we were able to write more quickly than using previous approaches, and the code was more clear. The library is already in wide use in research, industry, and teaching.},
DOI = {10.3390/info11020108}
}

@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}

@article{giannone2021illusion,
  title={Economic predictions with big data: The illusion of sparsity},
  author={Giannone, Domenico and Lenza, Michele and Primiceri, Giorgio E},
  journal={Econometrica},
  volume={89},
  number={5},
  pages={2409--2437},
  year={2021}
}

@article{doi:10.1146/annurev-economics-080217-053433,
author = {Athey, Susan and Imbens, Guido W.},
title = {Machine Learning Methods That Economists Should Know About},
journal = {Annual Review of Economics},
volume = {11},
number = {1},
pages = {685-725},
year = {2019},
doi = {10.1146/annurev-economics-080217-053433},

URL = { 
        https://doi.org/10.1146/annurev-economics-080217-053433
    
},
eprint = { 
        https://doi.org/10.1146/annurev-economics-080217-053433
    
}
,
    abstract = { We discuss the relevance of the recent machine learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods, and settings between the ML literature and the traditional econometrics and statistics literatures. Then we discuss some specific methods from the ML literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and classification, unsupervised learning methods, and matrix completion methods. Finally, we highlight newly developed methods at the intersection of ML and econometrics that typically perform better than either off-the-shelf ML or more traditional econometric methods when applied to particular classes of problems, including causal inference for average treatment effects, optimal policy estimation, and estimation of the counterfactual effect of price changes in consumer choice models. }
}


@article{doerr2021big,
  title={Big data and machine learning in central banking},
  author={Doerr, Sebastian and Gambacorta, Leonardo and Garralda, Jos{\'e} Maria Serena},
  journal={BIS Working Papers},
  number={930},
  year={2021},
  publisher={Bank for International Settlements}
}

@article{MLMedicalError,
    author = {Mullainathan, Sendhil and Obermeyer, Ziad},
    title = "{Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care*}",
    journal = {The Quarterly Journal of Economics},
    volume = {137},
    number = {2},
    pages = {679-727},
    year = {2021},
    month = {12},
    abstract = "{We use machine learning as a tool to study decision making, focusing specifically on how physicians diagnose heart attack. An algorithmic model of a patient’s probability of heart attack allows us to identify cases where physicians' testing decisions deviate from predicted risk. We then use actual health outcomes to evaluate whether those deviations represent mistakes or physicians’ superior knowledge. This approach reveals two inefficiencies. Physicians overtest: predictably low-risk patients are tested, but do not benefit. At the same time, physicians undertest: predictably high-risk patients are left untested, and then go on to suffer adverse health events including death. A natural experiment using shift-to-shift testing variation confirms these findings. Simultaneous over- and undertesting cannot easily be explained by incentives alone, and instead point to systematic errors in judgment. We provide suggestive evidence on the psychology underlying these errors. First, physicians use too simple a model of risk. Second, they overweight factors that are salient or representative of heart attack, such as chest pain. We argue health care models must incorporate physician error, and illustrate how policies focused solely on incentive problems can produce large inefficiencies.}",
    issn = {0033-5533},
    doi = {10.1093/qje/qjab046},
    url = {https://doi.org/10.1093/qje/qjab046},
    eprint = {https://academic.oup.com/qje/article-pdf/137/2/679/43336202/qjab046.pdf},
}
@article{Folklore,
    author = {Michalopoulos, Stelios and Xue, Melanie Meng},
    title = "{Folklore}",
    journal = {The Quarterly Journal of Economics},
    volume = {136},
    number = {4},
    pages = {1993-2046},
    year = {2021},
    month = {01},
    abstract = "{Folklore is the collection of traditional beliefs, customs, and stories of a community passed through the generations by word of mouth. We introduce to economics a unique catalog of oral traditions spanning approximately 1,000 societies. After validating the catalog’s content by showing that the groups’ motifs reflect known geographic and social attributes, we present two sets of applications. First, we illustrate how to fill in the gaps and expand upon a group’s ethnographic record, focusing on political complexity, high gods, and trade. Second, we discuss how machine learning and human classification methods can help shed light on cultural traits, using gender roles, attitudes toward risk, and trust as examples. Societies with tales portraying men as dominant and women as submissive tend to relegate their women to subordinate positions in their communities, both historically and today. More risk-averse and less entrepreneurial people grew up listening to stories wherein competitions and challenges are more likely to be harmful than beneficial. Communities with low tolerance toward antisocial behavior, captured by the prevalence of tricksters being punished, are more trusting and prosperous today. These patterns hold across groups, countries, and second-generation immigrants. Overall, the results highlight the significance of folklore in cultural economics, calling for additional applications.}",
    issn = {0033-5533},
    doi = {10.1093/qje/qjab003},
    url = {https://doi.org/10.1093/qje/qjab003},
    eprint = {https://academic.oup.com/qje/article-pdf/136/4/1993/40566512/qjab003.pdf},
}

@article{belloni2011inference,
  title={Inference for high-dimensional sparse econometric models},
  author={Belloni, Alexandre and Chernozhukov, Victor and Hansen, Christian},
  journal={arXiv preprint arXiv:1201.0220},
  year={2011}
}

@article{JMLR:v18:16-365,
author  = {Guillaume  Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas},
title   = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning},
journal = {Journal of Machine Learning Research},
year    = {2017},
volume  = {18},
number  = {17},
pages   = {1-5},
url     = {http://jmlr.org/papers/v18/16-365}
}

@inproceedings{sklearn_api,
  author    = {Lars Buitinck and Gilles Louppe and Mathieu Blondel and
               Fabian Pedregosa and Andreas Mueller and Olivier Grisel and
               Vlad Niculae and Peter Prettenhofer and Alexandre Gramfort
               and Jaques Grobler and Robert Layton and Jake VanderPlas and
               Arnaud Joly and Brian Holt and Ga{\"{e}}l Varoquaux},
  title     = {{API} design for machine learning software: experiences from the scikit-learn
               project},
  booktitle = {ECML PKDD Workshop: Languages for Data Mining and Machine Learning},
  year      = {2013},
  pages = {108--122},
}
@incollection{athey2018impact,
  title={The impact of machine learning on economics},
  author={Athey, Susan},
  booktitle={The economics of artificial intelligence: An agenda},
  pages={507--547},
  year={2018},
  publisher={University of Chicago Press}
}
@misc{syft,
      title={A generic framework for privacy preserving deep learning}, 
      author={Theo Ryffel and Andrew Trask and Morten Dahl and Bobby Wagner and Jason Mancuso and Daniel Rueckert and Jonathan Passerat-Palmbach},
      year={2018},
      eprint={1811.04017},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{predunequal,
author = {Fuster, Andreas and Goldsmith-Pinkham, Paul and Ramadorai, Tarun and Walther, Ansgar},
title = {Predictably Unequal? The Effects of Machine Learning on Credit Markets},
journal = {The Journal of Finance},
volume = {77},
number = {1},
pages = {5-47},
doi = {https://doi.org/10.1111/jofi.13090},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jofi.13090},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/jofi.13090},
abstract = {ABSTRACT Innovations in statistical technology in functions including credit-screening have raised concerns about distributional impacts across categories such as race. Theoretically, distributional effects of better statistical technology can come from greater flexibility to uncover structural relationships or from triangulation of otherwise excluded characteristics. Using data on U.S. mortgages, we predict default using traditional and machine learning models. We find that Black and Hispanic borrowers are disproportionately less likely to gain from the introduction of machine learning. In a simple equilibrium credit market model, machine learning increases disparity in rates between and within groups, with these changes attributable primarily to greater flexibility.},
year = {2022}
}
@article{maliar2021deep,
  title={Deep learning for solving dynamic economic models.},
  author={Maliar, Lilia and Maliar, Serguei and Winant, Pablo},
  journal={Journal of Monetary Economics},
  volume={122},
  pages={76--101},
  year={2021},
  publisher={Elsevier}
}
@Article{RePEc:fip:fedlrv:00023,
  author={Katrina Stierholz},
  title={{FRED®, the St. Louis Fed’s force of data}},
  journal={Review},
  year=2014,
  volume={96},
  number={2},
  pages={195-198},
  month={},
  keywords={},
  doi={},
  abstract={No history of the St. Louis Fed would be complete without a chapter on its leadership in providing economic data for the public. Today, this long-standing commitment to data service is encapsulated in one name: FRED.},
  url={https://ideas.repec.org/a/fip/fedlrv/00023.html}
}
@article{TidyData,
 title={Tidy Data},
 volume={59},
 url={https://www.jstatsoft.org/index.php/jss/article/view/v059i10},
 doi={10.18637/jss.v059.i10},
 abstract={A huge amount of effort is spent cleaning data to get it ready for analysis, but there has been little research on how to make data cleaning as easy and effective as possible. This paper tackles a small, but important, component of data cleaning: data tidying. Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table. This framework makes it easy to tidy messy datasets because only a small set of tools are needed to deal with a wide range of un-tidy datasets. This structure also makes it easier to develop tidy tools for data analysis, tools that both input and output tidy datasets. The advantages of a consistent data structure and matching tools are demonstrated with a case study free from mundane data manipulation chores.},
 number={10},
 journal={Journal of Statistical Software},
 author={Wickham, Hadley},
 year={2014},
 pages={1–23}
}

@article{varian2014big,
  title={Big data: New tricks for econometrics},
  author={Varian, Hal R},
  journal={Journal of Economic Perspectives},
  volume={28},
  number={2},
  pages={3--28},
  year={2014}
}

@Article{jrfm15040165,
AUTHOR = {Brotcke, Liming},
TITLE = {Time to Assess Bias in Machine Learning Models for Credit Decisions},
JOURNAL = {Journal of Risk and Financial Management},
VOLUME = {15},
YEAR = {2022},
NUMBER = {4},
ARTICLE-NUMBER = {165},
URL = {https://www.mdpi.com/1911-8074/15/4/165},
ISSN = {1911-8074},
ABSTRACT = {Focus on fair lending has become more intensified recently as bank and non-bank lenders apply artificial-intelligence (AI)-based credit determination approaches. The data analytics technique behind AI and machine learning (ML) has proven to be powerful in many application areas. However, ML can be less transparent and explainable than traditional regression models, which may raise unique questions about its compliance with fair lending laws. ML may also reduce potential for discrimination, by reducing discretionary and judgmental decisions. As financial institutions continue to explore ML applications in loan underwriting and pricing, the fair lending assessments typically led by compliance and legal functions will likely continue to evolve. In this paper, the author discusses unique considerations around ML in the existing fair lending risk assessment practice for underwriting and pricing models and proposes consideration of additional evaluations to be added in the present practice.},
DOI = {10.3390/jrfm15040165}
}




@article{kohlscheen2021does,
  title={What does machine learning say about the drivers of inflation?},
  author={Kohlscheen, Emanuel},
  journal={Available at SSRN 3949352},
  year={2021}
}

@article{chakraborty2017machine,
  title={Machine learning at central banks},
  author={Chakraborty, Chiranjit and Joseph, Andreas},
  year={2017},
  publisher={Bank of England working paper}
}
@article{BARREDOARRIETA202082,
title = {Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI},
journal = {Information Fusion},
volume = {58},
pages = {82-115},
year = {2020},
issn = {1566-2535},
doi = {https://doi.org/10.1016/j.inffus.2019.12.012},
url = {https://www.sciencedirect.com/science/article/pii/S1566253519308103},
author = {Alejandro {Barredo Arrieta} and Natalia Díaz-Rodríguez and Javier {Del Ser} and Adrien Bennetot and Siham Tabik and Alberto Barbado and Salvador Garcia and Sergio Gil-Lopez and Daniel Molina and Richard Benjamins and Raja Chatila and Francisco Herrera},
keywords = {Explainable Artificial Intelligence, Machine Learning, Deep Learning, Data Fusion, Interpretability, Comprehensibility, Transparency, Privacy, Fairness, Accountability, Responsible Artificial Intelligence},
abstract = {In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.}
}
@inproceedings{Parrots,
author = {Bender, Emily M. and Gebru, Timnit and McMillan-Major, Angelina and Shmitchell, Shmargaret},
title = {On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?},
year = {2021},
isbn = {9781450383097},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3442188.3445922},
doi = {10.1145/3442188.3445922},
abstract = {The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pretrained models and the methodology of fine-tuning them for specific tasks, researchers have extended the state of the art on a wide array of tasks as measured by leaderboards on specific benchmarks for English. In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.},
booktitle = {Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency},
pages = {610–623},
numpages = {14},
location = {Virtual Event, Canada},
series = {FAccT '21}
}

@techreport{fernandez2019financial,
  title={Financial frictions and the wealth distribution},
  author={Fern{\'a}ndez-Villaverde, Jes{\'u}s and Hurtado, Samuel and Nuno, Galo},
  year={2019},
  institution={National Bureau of Economic Research}
}

@book{DeepLearning,
    title={Deep Learning},
    author={Ian Goodfellow and Yoshua Bengio and Aaron Courville},
    publisher={MIT Press},
    note={http://www.deeplearningbook.org},
    year={2016}
}

@Manual{reticulate,
  title = {reticulate: Interface to 'Python'},
  author = {Kevin Ushey and JJ Allaire and Yuan Tang},
  year = {2022},
  note = {https://rstudio.github.io/reticulate/,
https://github.com/rstudio/reticulate},
}

@incollection{PyTorch,
title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {8024--8035},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}
}
@book{howard2020deep,
title={Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD},
author={Howard, J. and Gugger, S.},
isbn={9781492045526},
url={https://books.google.no/books?id=xd6LxgEACAAJ},
year={2020},
publisher={O'Reilly Media, Incorporated}
}
@article{breiman1996bagging,
  title={Bagging predictors},
  author={Breiman, Leo},
  journal={Machine learning},
  volume={24},
  number={2},
  pages={123--140},
  year={1996},
  publisher={Springer}
}
@techreport{ferreira2021forecasting,
  title={Forecasting with VAR-teXt and DFM-teXt Models: exploring the predictive power of central bank communication},
  author={Ferreira, Leonardo N},
  year={2021}
}

@article{duarte2018machine,
  title={Machine learning for continuous-time economics},
  author={Duarte, Victor},
  journal={Available at SSRN 3012602},
  year={2018}
}

@article{taylor2018forecasting,
  title={Forecasting at scale},
  author={Taylor, Sean J and Letham, Benjamin},
  journal={The American Statistician},
  volume={72},
  number={1},
  pages={37--45},
  year={2018},
  publisher={Taylor \& Francis}
}

@article{10.1257/jep.31.2.87,
Author = {Mullainathan, Sendhil and Spiess, Jann},
Title = {Machine Learning: An Applied Econometric Approach},
Journal = {Journal of Economic Perspectives},
Volume = {31},
Number = {2},
Year = {2017},
Month = {May},
Pages = {87-106},
DOI = {10.1257/jep.31.2.87},
URL = {https://www.aeaweb.org/articles?id=10.1257/jep.31.2.87}}


@inproceedings{xgboost, author = {Chen, Tianqi and Guestrin, Carlos}, title = {XGBoost: A Scalable Tree Boosting System}, year = {2016}, isbn = {9781450342322}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/2939672.2939785}, doi = {10.1145/2939672.2939785}, abstract = {Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning. More importantly, we provide insights on cache access patterns, data compression and sharding to build a scalable tree boosting system. By combining these insights, XGBoost scales beyond billions of examples using far fewer resources than existing systems.}, booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, pages = {785–794}, numpages = {10}, keywords = {large-scale machine learning}, location = {San Francisco, California, USA}, series = {KDD '16} }


@misc{TabularDeepLearning,
      title={Revisiting Deep Learning Models for Tabular Data}, 
      author={Yury Gorishniy and Ivan Rubachev and Valentin Khrulkov and Artem Babenko},
      year={2021},
      eprint={2106.11959},
      archivePrefix={arXiv},
      url={https://proceedings.neurips.cc/paper/2021/hash/9d86d83f925f2149e9edb0ac3b49229c-Abstract.html},
      primaryClass={cs.LG}
}

@misc{yi2022stock2vec,
      title={Stock2Vec: An Embedding to Improve Predictive Models for Companies}, 
      author={Ziruo Yi and Ting Xiao and Kaz-Onyeakazi Ijeoma and Ratnam Cheran and Yuvraj Baweja and Phillip Nelson},
      year={2022},
      eprint={2201.11290},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{problemmetrics,
      title={The Problem with Metrics is a Fundamental Problem for AI}, 
      author={Rachel Thomas and David Uminsky},
      year={2020},
      eprint={2002.08512},
      archivePrefix={arXiv},
      primaryClass={cs.CY}
}

@misc{BostonHousingEthicsProblem,
  title = {racist data destruction?},
  author={M Carlisle},
  howpublished = {\url{https://medium.com/@docintangible/racist-data-destruction-113e3eff54a8}},
  note = {Last accessed: 2020-02-08}
}

@article{Boston,
author = {Harrison, David and Rubinfeld, Daniel},
year = {1978},
month = {03},
pages = {81-102},
title = {Hedonic housing prices and the demand for clean air},
volume = {5},
journal = {Journal of Environmental Economics and Management},
doi = {10.1016/0095-0696(78)90006-2}
}

@misc{tensorflow2015-whitepaper,
title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
url={https://www.tensorflow.org/},
note={Software available from tensorflow.org},
author={
    Mart\'{i}n~Abadi and
    Ashish~Agarwal and
    Paul~Barham and
    Eugene~Brevdo and
    Zhifeng~Chen and
    Craig~Citro and
    Greg~S.~Corrado and
    Andy~Davis and
    Jeffrey~Dean and
    Matthieu~Devin and
    Sanjay~Ghemawat and
    Ian~Goodfellow and
    Andrew~Harp and
    Geoffrey~Irving and
    Michael~Isard and
    Yangqing Jia and
    Rafal~Jozefowicz and
    Lukasz~Kaiser and
    Manjunath~Kudlur and
    Josh~Levenberg and
    Dandelion~Man\'{e} and
    Rajat~Monga and
    Sherry~Moore and
    Derek~Murray and
    Chris~Olah and
    Mike~Schuster and
    Jonathon~Shlens and
    Benoit~Steiner and
    Ilya~Sutskever and
    Kunal~Talwar and
    Paul~Tucker and
    Vincent~Vanhoucke and
    Vijay~Vasudevan and
    Fernanda~Vi\'{e}gas and
    Oriol~Vinyals and
    Pete~Warden and
    Martin~Wattenberg and
    Martin~Wicke and
    Yuan~Yu and
    Xiaoqiang~Zheng},
  year={2015},
}

@misc{chollet2015keras,
  title={Keras},
  author={Chollet, Fran\c{c}ois and others},
  year={2015},
  howpublished={\url{https://keras.io}},
}

@misc{onnx,
    author = {Bai, Junjie and Lu, Fang and Zhang, Ke and others},
    title = {ONNX: Open Neural Network Exchange},
    year = {2019},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/onnx/onnx}},
    commit = {94d238d96e3fb3a7ba34f03c284b9ad3516163be}
}

@article{breiman2001random,
  title={Random forests},
  author={Breiman, Leo},
  journal={Machine learning},
  volume={45},
  number={1},
  pages={5--32},
  year={2001},
  publisher={Springer}
}

@article{BARRO19941,
title = {Sources of economic growth},
journal = {Carnegie-Rochester Conference Series on Public Policy},
volume = {40},
pages = {1-46},
year = {1994},
issn = {0167-2231},
doi = {10.1016/0167-2231(94)90002-7},
url = {https://www.sciencedirect.com/science/article/pii/0167223194900027},
author = {Robert J. Barro and Jong-Wha Lee},
abstract = {For 116 countries from 1965 to 1985, the lowest quintile had an average growth rate of real per capita GDP of - 1.3pct, whereas the highest quintile had an average of 4.8pct. We isolate five influences that discriminate reasonably well between the slow-and fast-growers: a conditional convergence effect, whereby a country grows faster if it begins with lower real per-capita GDP relative to its initial level of human capital in the forms of educational attainment and health; a positive effect on growth from a high ratio of investment to GDP (although this effect is weaker than that reported in some previous studies); a negative effect from overly large government; a negative effect from government-induced distortions of markets; and a negative effect from political instability. Overall, the fitted growth rates for 85 countries for 1965–1985 had a correlation of 0.8 with the actual values. We also find that female educational attainment has a pronounced negative effect on fertility, whereas female and male attainment are each positively related to life expectancy and negatively related to infant mortality. Male attainment plays a positive role in primary-school enrollment ratios, and male and female attainment relate positively to enrollment at the secondary level.}
}

@book{géron2017hands-on,
  address = {Sebastopol, CA},
  author = {Géron, Aurélien},
  biburl = {https://www.bibsonomy.org/bibtex/2a91270a3a516f4edaa5d459c40317fcc/achakraborty},
  interhash = {e2bd4a803c6cba6cca1d926b393806ad},
  intrahash = {a91270a3a516f4edaa5d459c40317fcc},
  isbn = {978-1491962299},
  keywords = {2019 book machine-learning oreilly tensorflow textbook},
  publisher = {O'Reilly Media},
  title = {Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems, 2nd edition},
  year = 2019
}

@techreport{BISstatQR2017,
title =  {Recent enhancements to the BIS statistics},
author = {{Bank for International Settlements}},
url = {https://www.bis.org/publ/qtrpdf/r_qt1709c.htm},
volume = {September},
year = {2017},
journal={BIS Quarterly Review}
}

@article{rossi2013exchange,
  title={Exchange rate predictability},
  author={Rossi, Barbara},
  journal={Journal of economic literature},
  volume={51},
  number={4},
  pages={1063--1119},
  year={2013}
}

@article{hollo2012ciss,
  title={CISS-a composite indicator of systemic stress in the financial system},
  author={Hollo, Daniel and Kremer, Manfred and Lo Duca, Marco},
  year={2012},
  publisher={ECB Working paper}
}

@misc{biscbspeeches,
    author = {Bank for International Settlements},
    title = {Central bank speeches},
    year = {2024},
    url = {https://www.bis.org/cbspeeches/download.htm}
}

@article{emcbcom,
    author = {Tatiana Evdokimova and Piroska Nagy Mohácsi and Olga Ponomarenko and Elina Ribakova},
    title = {Central banks and policy communication: How emerging markets have outperformed the Fed and ECB},
    year = {2023},
    institution = {Peterson Institute for International Economics},
    url = {https://www.piie.com/publications/working-papers/central-banks-and-policy-communication-how-emerging-markets-have}
}

@article{BARONGO2024100511,
    author = {Rweyemamu Ignatius Barongo and Jimmy Tibangayuka Mbelwa},
    title = {Using machine learning for detecting liquidity risk in banks},
    journal = {Machine Learning with Applications},
    volume = {15},
    year = {2024},
    doi = {https://doi.org/10.1016/j.mlwa.2023.100511},
    url = {https://www.sciencedirect.com/science/article/pii/S2666827023000646},
}