@article{nori2019interpretml,
  title={InterpretML: A Unified Framework for Machine Learning Interpretability},
  author={Nori, Harsha and Jenkins, Samuel and Koch, Paul and Caruana, Rich},
  journal={arXiv preprint arXiv:1909.09223},
  year={2019}
}
    

@inproceedings{caruana2015intelligible,
  title={Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission},
  author={Caruana, Rich and Lou, Yin and Gehrke, Johannes and Koch, Paul and Sturm, Marc and Elhadad, Noemie},
  booktitle={Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  pages={1721--1730},
  year={2015},
  organization={ACM}
}
    

@inproceedings{lou2013accurate,
  title={Accurate intelligible models with pairwise interactions},
  author={Lou, Yin and Caruana, Rich and Gehrke, Johannes and Hooker, Giles},
  booktitle={Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining},
  pages={623--631},
  year={2013},
  organization={ACM}
}
    

@inproceedings{lou2012intelligible,
  title={Intelligible models for classification and regression},
  author={Lou, Yin and Caruana, Rich and Gehrke, Johannes},
  booktitle={Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining},
  pages={150--158},
  year={2012},
  organization={ACM}
}
    

@article{wang2022interpretability,
  title={Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values},
  author={Wang, Zijie J and Kale, Alex and Nori, Harsha and Stella, Peter and Nunnally, Mark E and Chau, Duen Horng and Vorvoreanu, Mihaela and Vaughan, Jennifer Wortman and Caruana, Rich},
  journal={arXiv preprint arXiv:2206.15465},
  year={2022}
}
    

@inproceedings{zhang2019axiomatic,
  title={Axiomatic Interpretability for Multiclass Additive Models},
  author={Zhang, Xuezhou and Tan, Sarah and Koch, Paul and Lou, Yin and Chajewska, Urszula and Caruana, Rich},
  booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={226--234},
  year={2019},
  organization={ACM}
}
    

@inproceedings{tan2018distill,
  title={Distill-and-compare: auditing black-box models using transparent model distillation},
  author={Tan, Sarah and Caruana, Rich and Hooker, Giles and Lou, Yin},
  booktitle={Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society},
  pages={303--310},
  year={2018},
  organization={ACM}
}
    

@article{lengerich2019purifying,
  title={Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models},
  author={Lengerich, Benjamin and Tan, Sarah and Chang, Chun-Hao and Hooker, Giles and Caruana, Rich},
  journal={arXiv preprint arXiv:1911.04974},
  year={2019}
}
    

@inproceedings{kaur2020interpreting,
  title={Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning},
  author={Kaur, Harmanpreet and Nori, Harsha and Jenkins, Samuel and Caruana, Rich and Wallach, Hanna and Wortman Vaughan, Jennifer},
  booktitle={Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems},
  pages={1--14},
  year={2020}
}
    

@article{chang2020interpretable,
  title={How Interpretable and Trustworthy are GAMs?},
  author={Chang, Chun-Hao and Tan, Sarah and Lengerich, Ben and Goldenberg, Anna and Caruana, Rich},
  journal={arXiv preprint arXiv:2006.06466},
  year={2020}
}
    

@inproceedings{pmlr-v139-nori21a,
  title = 	 {Accuracy, Interpretability, and Differential Privacy via Explainable Boosting},
  author =       {Nori, Harsha and Caruana, Rich and Bu, Zhiqi and Shen, Judy Hanwen and Kulkarni, Janardhan},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {8227--8237},
  year = 	 {2021},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  publisher =    {PMLR}
}
    

@inproceedings{ribeiro2016should,
  title={Why should i trust you?: Explaining the predictions of any classifier},
  author={Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
  booktitle={Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining},
  pages={1135--1144},
  year={2016},
  organization={ACM}
}
    

@incollection{NIPS2017_7062,
 title = {A Unified Approach to Interpreting Model Predictions},
 author = {Lundberg, Scott M and Lee, Su-In},
 booktitle = {Advances in Neural Information Processing Systems 30},
 editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
 pages = {4765--4774},
 year = {2017},
 publisher = {Curran Associates, Inc.},
 url = {https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf}
}
    

@article{lundberg2018consistent,
  title={Consistent individualized feature attribution for tree ensembles},
  author={Lundberg, Scott M and Erion, Gabriel G and Lee, Su-In},
  journal={arXiv preprint arXiv:1802.03888},
  year={2018}
}
    

@article{lundberg2018explainable,
  title={Explainable machine-learning predictions for the prevention of hypoxaemia during surgery},
  author={Lundberg, Scott M and Nair, Bala and Vavilala, Monica S and Horibe, Mayumi and Eisses, Michael J and Adams, Trevor and Liston, David E and Low, Daniel King-Wai and Newman, Shu-Fang and Kim, Jerry and others},
  journal={Nature Biomedical Engineering},
  volume={2},
  number={10},
  pages={749},
  year={2018},
  publisher={Nature Publishing Group}
}
    

@article{herman2017salib,
  title={SALib: An open-source Python library for Sensitivity Analysis.},
  author={Herman, Jonathan D and Usher, Will},
  journal={J. Open Source Software},
  volume={2},
  number={9},
  pages={97},
  year={2017}
}
    

@article{morris1991factorial,
  title={},
  author={Morris, Max D},
  journal={Technometrics},
  volume={33},
  number={2},
  pages={161--174},
  year={1991},
  publisher={Taylor \& Francis Group}
}
    

@article{friedman2001greedy,
  title={Greedy function approximation: a gradient boosting machine},
  author={Friedman, Jerome H},
  journal={Annals of statistics},
  pages={1189--1232},
  year={2001},
  publisher={JSTOR}
}
    

@article{pedregosa2011scikit,
  title={Scikit-learn: Machine learning in Python},
  author={Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others},
  journal={Journal of machine learning research},
  volume={12},
  number={Oct},
  pages={2825--2830},
  year={2011}
}
    

@online{plotly, 
  author = {Plotly Technologies Inc.}, 
  title = {Collaborative data science}, 
  publisher = {Plotly Technologies Inc.}, 
  address = {Montreal, QC}, 
  year = {2015}, 
  url = {https://plot.ly}
}
    

@article{varoquaux2009joblib,
  title={Joblib: running python function as pipeline jobs},
  author={Varoquaux, Ga{\"e}l and Grisel, O},
  journal={packages. python. org/joblib},
  year={2009}
}