refs.bib

@inproceedings{lime,
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               Sameer Singh and
               Carlos Guestrin},
  title     = {{"Why Should {I} Trust You?": Explaining the Predictions of Any Classifier}},
  booktitle = {Proceedings of the 22nd {ACM} {SIGKDD} International Conference on
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  pages     = {1135--1144},
  year      = {2016},
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}


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@incollection{NIPS2017_7062,
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author = {Lundberg, Scott M and Lee, Su-In},
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publisher = {Curran Associates, Inc.},
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}


@article{iBreakDown,
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    author={Alicja Gosiewska and Przemyslaw Biecek},
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@Manual{ingredients,
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@article{Business,
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@book{molnar2019,
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  year       = {2019},
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	journal = {Proceedings of the National Academy of Sciences}
}


@book{ema,
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  author     = {Przemysław Biecek and Tomasz Burzykowski},
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  year       = {2020},
  subtitle   = {Explore, Explain and Examine Predictive Models}
}



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@article{XAI,
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keywords = "Explainable Artificial Intelligence, Machine Learning, Deep Learning, Data Fusion, Interpretability, Comprehensibility, Transparency, Privacy, Fairness, Accountability, Responsible Artificial Intelligence"}



@article{aix,
  author  = {Vijay Arya and Rachel K. E. Bellamy and Pin-Yu Chen and Amit Dhurandhar and Michael Hind and Samuel C. Hoffman and Stephanie Houde and Q. Vera Liao and Ronny Luss and Aleksandra Mojsilović and Sami Mourad and Pablo Pedemonte and Ramya Raghavendra and John T. Richards and Prasanna Sattigeri and Karthikeyan Shanmugam and Moninder Singh and Kush R. Varshney and Dennis Wei and Yunfeng Zhang},
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  year    = {2020},
  volume  = {21},
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  pages   = {1-6},
  url     = {http://jmlr.org/papers/v21/19-1035.html}
}


@article{interpretml,
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}

@incollection{MAPLE,
title = {{Model Agnostic Supervised Local Explanations}},
author = {Plumb, Gregory and Molitor, Denali and Talwalkar, Ameet S},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {2515--2524},
year = {2018},
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url = {http://papers.nips.cc/paper/7518-model-agnostic-supervised-local-explanations.pdf}
}

@article{doi:10.1177/1473871620904671,
author = {Angelos Chatzimparmpas and Rafael M. Martins and Ilir Jusufi and Andreas Kerren},
title ={{A survey of surveys on the use of visualization for interpreting machine learning models}},
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volume = {19},
number = {3},
pages = {207-233},
year = {2020},
URL = {https://doi.org/10.1177/1473871620904671},
eprint = {https://doi.org/10.1177/1473871620904671}
}


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author = "Shixia Liu and Xiting Wang and Mengchen Liu and Jun Zhu",
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}


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@article{dudley2018review,
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@article{choo2018visual,
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@article{GARCIA201830,
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}rresen and João Luiz [Dihl Comba]}},
}


@article{DBLP:journals/corr/abs-1801-06889,
  title={{Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers}},
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}

@article{sacha2016visual,
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}

@Article{pdp,
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  }
  
@Manual{limeR,
    title = {{lime: Local Interpretable Model-Agnostic Explanations}},
    author = {Thomas Lin Pedersen and Michaël Benesty},
    year = {2019},
    note = {R package version 0.5.1},
    url = {https://CRAN.R-project.org/package=lime},
}

@misc{PatrickHall,
  author = {Patrick Hall},
  title = {awesome-machine-learning-interpretability},
  year = {2020}, 
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/jphall663/awesome-machine-learning-interpretability}},
  note = {Access: 2020-07-01}
}

@inproceedings{10.1145/2783258.2788613,
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    booktitle = {Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
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    numpages = {10},
    keywords = {intelligibility, additive models, risk prediction, healthcare, interaction detection, logistic regression, classification},
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    series = {KDD ’15}
}
  

@misc{eli5,
  author = {Mikhail Korobov and Konstantin Lopuhin},
  title = {{ELI5}},
  year = {2020},
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@book{hall2018introduction,
  title={{An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI}},
  author={Hall, P. and Gill, N.},
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  url={https://books.google.pl/books?id=vilpuwEACAAJ},
  year={2018},
  publisher={O'Reilly Media}
}

@article{hall2018art,
    title={{On the Art and Science of Machine Learning Explanations}},
    author={Patrick Hall},
    year={2018},
    journal = {arXiv},
    eprint={1810.02909},
    archivePrefix={arXiv},
    primaryClass={stat.ML},
    url = {https://arxiv.org/abs/1810.02909}
}

@article{RJ-2018-072,
  author = {Mateusz Staniak and Przemysław Biecek},
  title = {{Explanations of Model Predictions with live and breakDown
          Packages}},
  year = {2018},
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  url = {https://doi.org/10.32614/RJ-2018-072},
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  volume = {10},
  number = {2}
}

@inproceedings{XAItaxonomies,
title = {{Explaining Explanations: An Overview of Interpretability of Machine Learning}},
author = {Gilpin, Leilani and Bau, David and Yuan, Ben and Bajwa, Ayesha and Specter, Michael and Kagal, Lalana},
year = {2018},
month = {10},
pages = {80-89},
url = {https://doi.org/10.1109/DSAA.2018.00018}
}

@inproceedings{biran2017explanation,
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  author={Biran, Or and Cotton, Courtenay},
  booktitle={IJCAI-17 workshop on explainable AI (XAI)},
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  number={1},
  pages={8--13},
  year={2017}
}

  @Article{titanic,
    title = {{Measuring the Stability of Results from Supervised
      Statistical Learning}},
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author = {Nature},
title={Ascent of machine learning in medicine},
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year={2019},
month={May},
day={01},
volume={18},
number={5},
pages={407-407},
abstract={Machine learning is swiftly infiltrating many areas within the healthcare industry, from diagnosis and prognosis to drug development and epidemiology, with significant potential to transform the medical landscape.},
issn={1476-4660},
url={https://doi.org/10.1038/s41563-019-0360-1}
}

@article{med2,
author = {Obermeyer, Ziad and Emanuel, Ezekiel J.},
title = {{Predicting the Future — Big Data, Machine Learning, and Clinical Medicine}},
journal = {New England Journal of Medicine},
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number = {13},
pages = {1216-1219},
year = {2016},
note ={PMID: 27682033},
URL = {https://doi.org/10.1056/NEJMp1606181},
eprint = { https://doi.org/10.1056/NEJMp1606181}
}

  @Article{iml,
    author = {Christoph Molnar and Bernd Bischl and Giuseppe Casalicchio},
    title = {{iml: An R package for Interpretable Machine Learning}},
    url = {http://doi.org/10.21105/joss.00786},
    year = {2018},
    publisher = {Journal of Open Source Software},
    volume = {3},
    number = {26},
    pages = {786},
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  }
  
  @Manual{DALEXtra,
    title = {{DALEXtra: Extension for 'DALEX' Package}},
    author = {Szymon Maksymiuk and Przemyslaw Biecek},
    year = {2020},
    note = {R package version 2.0.0},
    url = {https://CRAN.R-project.org/package=DALEXtra},
  }
  
   @Manual{deepdep,
    title = {{deepdep: Visualise and Explore the Deep Dependencies of R Packages}},
    author = {Dominik Rafacz and Hubert Baniecki and Szymon Maksymiuk and Mateusz Bakala},
    year = {2020},
    note = {R package version 0.2.1},
    url = {https://CRAN.R-project.org/package=deepdep},
  }
  
 @article{loco,
author = {Jing Lei and Max G’Sell and Alessandro Rinaldo and Ryan J. Tibshirani and Larry Wasserman},
title = {{Distribution-Free Predictive Inference for Regression}},
journal = {Journal of the American Statistical Association},
volume = {113},
number = {523},
pages = {1094-1111},
year  = {2018},
publisher = {Taylor & Francis},
URL = {https://doi.org/10.1080/01621459.2017.1307116},
eprint = { https://doi.org/10.1080/01621459.2017.1307116}

}

@article{ICE,
author = {Alex Goldstein and Adam Kapelner and Justin Bleich and Emil Pitkin},
title = {{Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation}},
journal = {Journal of Computational and Graphical Statistics},
volume = {24},
number = {1},
pages = {44-65},
year  = {2015},
publisher = {Taylor & Francis},
URL = { https://doi.org/10.1080/10618600.2014.907095},
eprint = {https://doi.org/10.1080/10618600.2014.907095}
}
@Manual{ALEPlot,
    title = {{ALEPlot: Accumulated Local Effects (ALE) Plots and Partial Dependence
(PD) Plots}},
    author = {Dan Apley},
    year = {2018},
    note = {R package version 1.1},
    url = {https://CRAN.R-project.org/package=ALEPlot},
  }

@Manual{EIX,
    title = {{EIX: Explain Interactions in 'XGBoost'}},
    author = {Ewelina Karbowiak and Przemyslaw Biecek},
    year = {2020},
    note = {R package version 1.1},
    url = {https://CRAN.R-project.org/package=EIX},
  }
  
@Manual{ExplainPrediction,
    title = {{ExplainPrediction: Explanation of Predictions for Classification and Regression
Models}},
    author = {Marko Robnik-Sikonja},
    year = {2018},
    note = {R package version 1.3.0},
    url = {https://CRAN.R-project.org/package=ExplainPrediction},
  }
  
 @Manual{fairness,
    title = {{fairness: Algorithmic Fairness Metrics}},
    author = {Nikita Kozodoi and Tibor {V. Varga}},
    year = {2020},
    note = {R package version 1.1.1},
    url = {https://CRAN.R-project.org/package=fairness},
  }
  
 @Manual{fastshap,
    title = {{fastshap: Fast Approximate Shapley Values}},
    author = {Brandon Greenwell},
    year = {2020},
    note = {R package version 0.0.5},
    url = {https://CRAN.R-project.org/package=fastshap},
  }
  
  @Manual{flashlight,
    title = {{flashlight: Shed Light on Black Box Machine Learning Models}},
    author = {Michael Mayer},
    year = {2020},
    note = {R package version 0.7.0},
    url = {https://CRAN.R-project.org/package=flashlight},
  }
  
@Manual{forestmodel,
    title = {{forestmodel: Forest Plots from Regression Models}},
    author = {Nick Kennedy},
    year = {2020},
    note = {R package version 0.6.2},
    url = {https://CRAN.R-project.org/package=forestmodel},
  }  

@Article{fscaret,
    title = {{Heuristic modeling of macromolecule release from PLGA microspheres.}},
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    number = {1},
    pages = {4601-4611},
    URL = {http://doi.org/10.2147/IJN.S53364}
  }
  
@Manual{lime-pkg,
    title = {{lime: Local Interpretable Model-Agnostic Explanations}},
    author = {Thomas Lin Pedersen and Michaël Benesty},
    year = {2019},
    note = {R package version 0.5.1},
    url = {https://CRAN.R-project.org/package=lime},
  }
  
@Manual{mcr,
    title = {{mcr: Method Comparison Regression}},
    author = {Ekaterina Manuilova and Andre Schuetzenmeister and Fabian Model},
    year = {2014},
    note = {R package version 1.2.1},
    url = {https://CRAN.R-project.org/package=mcr},
  }
  

  
 @article{modelDown,
  doi = {10.21105/joss.01444},
  url = {https://doi.org/10.21105/joss.01444},
  year = {2019},
  publisher = {The Open Journal},
  volume = {4},
  number = {38},
  pages = {1444},
  author = {Kamil Romaszko and Magda Tatarynowicz and Mateusz Urbański and Przemysław Biecek},
  title = {{modelDown: automated website generator with interpretable documentation for predictive machine learning models}},
  journal = {Journal of Open Source Software}
}

  
@Article{modelStudio,
    author = {Hubert Baniecki and Przemyslaw Biecek},
    title = {{{modelStudio}: Interactive Studio with Explanations for {ML} Predictive Models}},
    url = {https://doi.org/10.21105/joss.01798},
    year = {2019},
    month = {Nov},
    volume = {4},
    number = {43},
    pages = {1798},
    publisher = {The Open Journal},
    journal = {Journal of Open Source Software},
  }

@Manual{randomForestExplainer,
    title = {{randomForestExplainer: Explaining and Visualizing Random Forests in Terms of Variable
Importance}},
    author = {Aleksandra Paluszynska and Przemyslaw Biecek and Yue Jiang},
    year = {2020},
    note = {R package version 0.10.1},
    url = {https://CRAN.R-project.org/package=randomForestExplainer},
  }
  
@Manual{shapper,
    title = {{shapper: Wrapper of Python Library 'shap'}},
    author = {Szymon Maksymiuk and Alicja Gosiewska and Przemyslaw Biecek},
    year = {2019},
    note = {R package version 0.1.2},
    url = {https://CRAN.R-project.org/package=shapper},
  }


@misc{skater,
    author = {{Oracle and contributors}},
    title = {skater},
    year = {2020},
    version = {1.1.2b1},
    url= {https://github.com/datascienceinc/skater/}
}

@Manual{smbinning,
    title = {{smbinning: Scoring Modeling and Optimal Binning}},
    author = {Herman Jopia},
    year = {2019},
    note = {R package version 0.9},
    url = {https://CRAN.R-project.org/package=smbinning},
  }
  
  
 @article{survxai,
  doi = {10.21105/joss.00961},
  url = {https://doi.org/10.21105/joss.00961},
  year = {2018},
  publisher = {The Open Journal},
  volume = {3},
  number = {31},
  pages = {961},
  author = {Aleksandra Grudziaz and Alicja Gosiewska and Przemyslaw Biecek},
  title = {{survxai: an R package for structure-agnostic explanations of survival models}},
  journal = {Journal of Open Source Software}
}

  
@Manual{vip,
    title = {{vip: Variable Importance Plots}},
    author = {Brandon Greenwell and Brad Boehmke and Bernie Gray},
    year = {2020},
    note = {R package version 0.2.2},
    url = {https://CRAN.R-project.org/package=vip},
  }
  
@Manual{vivo,
    title = {{vivo: Variable Importance via Oscillations}},
    author = {Anna Kozak and Przemyslaw Biecek},
    year = {2020},
    note = {R package version 0.2.0},
    url = {https://CRAN.R-project.org/package=vivo},
  }
  

@article{breiman2001statistical,
author = "Breiman, Leo",
doi = "10.1214/ss/1009213726",
fjournal = "Statistical Science",
journal = "Statist. Sci.",
month = "08",
number = "3",
pages = "199--231",
publisher = "The Institute of Mathematical Statistics",
title = "Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)",
url = "https://doi.org/10.1214/ss/1009213726",
volume = "16",
year = "2001"
}


  @Manual{corrgrapher,
    title = {{corrgrapher: Explore Correlations Between Variables in a Machine Learning Model}},
    author = {Pawel Morgen and Przemyslaw Biecek},
    year = {2020},
    note = {R package version 1.0.2},
    url = {https://CRAN.R-project.org/package=corrgrapher},
  }
  
    @Manual{triplot,
    title = {{triplot: Explaining Correlated Features in Machine Learning Models}},
    author = {Katarzyna Pekala and Przemyslaw Biecek},
    year = {2020},
    note = {R package version 1.3.0},
    url = {https://CRAN.R-project.org/package=triplot},
  }
  
 @article{shapr,
  url = {https://doi.org/10.21105/joss.02027},
  year = {2019},
  publisher = {The Open Journal},
  volume = {5},
  number = {46},
  pages = {2027},
  author = {Nikolai Sellereite and Martin Jullum},
  title = {{shapr: An R-package for explaining machine learning models with dependence-aware Shapley values}},
  journal = {Journal of Open Source Software}
}

  @Manual{fairmodels,
    title = {{fairmodels: Flexible Tool for Bias Detection, Visualization, and Mitigation}},
    author = {Jakub Wiśniewski and Przemysław Biecek},
    year = {2020},
    note = {R package version 0.1.1},
    url = {https://CRAN.R-project.org/package=fairmodels},
  }

  @Manual{aif360,
    title = {{aif360: Help Detect and Mitigate Bias in Machine Learning Models}},
    author = {Gabriela {de Queiroz} and Stacey Ronaghan and Saishruthi Swaminathan},
    year = {2020},
    note = {R package version 0.1.0},
    url = {https://CRAN.R-project.org/package=aif360},
  }
  
    @Manual{fairml,
    title = {{fairml: Fair Models in Machine Learning}},
    author = {Marco Scutari},
    year = {2020},
    note = {R package version 0.2},
    url = {https://CRAN.R-project.org/package=fairml},
  }
  
    @Manual{arenar,
    title = {{arenar: Arena for the Exploration and Comparison of any ML Models}},
    author = {Piotr Piątyszek and Przemyslaw Biecek},
    year = {2020},
    note = {R package version 0.1.8},
    url = {https://CRAN.R-project.org/package=arenar},
  }
  
  
    @Article{glmnet,
    title = {{Regularization Paths for Generalized Linear Models via
      Coordinate Descent}},
    author = {Jerome Friedman and Trevor Hastie and Robert Tibshirani},
    journal = {Journal of Statistical Software},
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    volume = {33},
    number = {1},
    pages = {1--22},
    url = {https://doi.org/10.18637/jss.v033.i01},
  }
  
  @Manual{naivebayes,
    title = {{naivebayes: High Performance Implementation of the Naive Bayes Algorithm in R}},
    author = {Michal Majka},
    year = {2019},
    note = {R package version 0.9.7},
    url = {https://CRAN.R-project.org/package=naivebayes},
  }
  
    @Manual{kknn,
    title = {{kknn: Weighted k-Nearest Neighbors}},
    author = {Klaus Schliep and Klaus Hechenbichler},
    year = {2016},
    note = {R package version 1.3.1},
    url = {https://CRAN.R-project.org/package=kknn},
  }
  
    @Article{partyctree,
    title = {{Unbiased Recursive Partitioning: A Conditional Inference
      Framework}},
    author = {Torsten Hothorn and Kurt Hornik and Achim Zeileis},
    journal = {Journal of Computational and Graphical Statistics},
    year = {2006},
    volume = {15},
    number = {3},
    pages = {651--674},
    URL = {http://doi.org/10.1198/106186006X133933}
  }
  
   @Book{knn,
    title = {{Modern Applied Statistics with S}},
    author = {W. N. Venables and B. D. Ripley},
    publisher = {Springer},
    edition = {Fourth},
    address = {New York},
    year = {2002},
    note = {ISBN 0-387-95457-0},
    url = {http://www.stats.ox.ac.uk/pub/MASS4},
  }
  
    @Article{arules,
    title = {{The arules R-Package Ecosystem: Analyzing Interesting
      Patterns from Large Transaction Datasets}},
    author = {Michael Hahsler and Sudheer Chelluboina and Kurt Hornik
      and Christian Buchta},
    year = {2011},
    journal = {Journal of Machine Learning Research},
    volume = {12},
    pages = {1977--1981},
    url = {http://jmlr.csail.mit.edu/papers/v12/hahsler11a.html},
  }
  
  
  @article{arulescba,
  author = {Michael Hahsler and Ian Johnson and Tomáš Kliegr and
          Jaroslav Kuchař},
  title = {{Associative Classification in R: arc, arulesCBA, and rCBA}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-048},
  url = {https://doi.org/10.32614/RJ-2019-048},
  pages = {254--267},
  volume = {11},
  number = {2}
}
  
  
  @article{arulesviz,
  author = {Michael Hahsler},
  title = {{arulesViz: Interactive Visualization of Association Rules
          with R}},
  year = {2017},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2017-047},
  url = {https://doi.org/10.32614/RJ-2017-047},
  pages = {163--175},
  volume = {9},
  number = {2}
}


@misc{rashomon,
   author = {Wikipedia},
   title = {Rashomon effect},
   year = {2020},
   url = {https://en.wikipedia.org/wiki/Rashomon_effect},
   note = {Online; accessed 22-09-2020}
 }
  
  @article{mlr3,
  url = {https://doi.org/10.21105/joss.01903},
  year = {2019},
  publisher = {The Open Journal},
  volume = {4},
  number = {44},
  pages = {1903},
  author = {Michel Lang and Martin Binder and Jakob Richter and Patrick Schratz and Florian Pfisterer and Stefan Coors and Quay Au and Giuseppe Casalicchio and Lars Kotthoff and Bernd Bischl},
  title = {mlr3: A modern object-oriented machine learning framework in R},
  journal = {Journal of Open Source Software}
}

  
    @Manual{tidymodels,
    title = {{Tidymodels: a collection of packages for modeling and
      machine learning using tidyverse principles.}},
    author = {Max Kuhn and Hadley Wickham},
    url = {https://www.tidymodels.org},
    year = {2020},
  }
  
    @Manual{darpa,
    title = {{Explainable Artificial Intelligence (XAI)}},
    author = {David Gunning},
    url = {https://www.darpa.mil/attachments/XAIProgramUpdate.pdf},
    year = {2017},
    bote = {DARPA/I2O}
  }
  
  @article{responsibleml,
  title={{A Responsible Machine Learning Workflow with Focus on Interpretable Models, Post-hoc Explanation, and Discrimination Testing}},
  author={Gill, Navdeep and Hall, Patrick and Montgomery, Kim and Schmidt, Nicholas},
  journal={Information},
  volume={11},
  number={3},
  pages={137},
  year={2020},
  publisher={Multidisciplinary Digital Publishing Institute},
  URL = {http://doi.org/10.3390/info11030137}
}

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

@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}
}

@article{Goodman_Flaxman_2017, 
  title     = {European Union Regulations on Algorithmic Decision-Making and a “Right to Explanation”}, 
  volume    = {38}, 
  url       = {http://doi.org/10.1609/aimag.v38i3.2741}, 
  number    = {3}, 
  journal   = {AI Magazine}, 
  author    = {Goodman, Bryce and Flaxman, Seth}, 
  year      = {2017}, 
  month     = {Oct.}, 
  pages     = {50-57} 
}

  @Manual{treeshap,
    title = {treeshap: Fast shap values computations for ensamble models},
    author = {Konrad Komisarczyk and Pawel Kozminski and Przemyslaw Biecek},
    year = {2020},
    note = {R package version 0.0.0.9000},
    url = {https://github.com/ModelOriented/treeshap},
  }
  
    @Manual{SHAPforxgboost,
    title = {SHAPforxgboost: SHAP Plots for 'XGBoost'},
    author = {Yang Liu and Allan Just},
    year = {2020},
    note = {R package version 0.0.4},
    url = {https://CRAN.R-project.org/package=SHAPforxgboost},
  }
  
  @Manual{xai2cloud,
    title = {xai2cloud: Deploys An Explainer To The Cloud},
    author = {Adam Rydelek},
    year = {2020},
    note = {https://github.com/ModelOriented/xai2cloud},
  }