kdd_2019.bib

@article{fair_washing,
	title = {Fairwashing: the {R}isk of {R}ationalization},
	author = {A{\"\i}vodji, Ulrich and Arai, Hiromi and Fortineau, Olivier and Gambs, S{\'e}bastien and Hara, Satoshi and Tapp, Alain},
	journal = {arXiv preprint arXiv:1901.09749},
	year = {2019}, 
	note = {URL: \url{https://arxiv.org/pdf/1901.09749.pdf}}}

@article{security_of_ml,
  	title={The {S}ecurity of {M}achine {L}earning},
 	author={Barreno, Marco and Nelson, Blaine and Joseph, Anthony D. and Tygar, J. Doug},
  	journal={Machine Learning},
  	volume={81},
  	number={2},
  	pages={121--148},
  	year={2010},
  	publisher={Springer},
	note={URL: \url{https://people.eecs.berkeley.edu/~adj/publications/paper-files/SecML-MLJ2010.pdf}}}

@article{dt_surrogate2,
	title={Interpreting {B}lackbox {M}odels via {M}odel {E}xtraction},
	author={Osbert Bastani and Carolyn Kim and Hamsa Bastani},
	journal={arXiv preprint arXiv:1705.08504},
	note={URL: \url{https://arxiv.org/pdf/1705.08504.pdf}},
	year={2017}}

@book{cart,
  	title={Classification and Regression Trees},
  	author={Breiman, Leo and Friedman, Jerome H. and Olshen, Richard A. and Stone, Charles J.},
  	year={1984},
  	publisher={Routledge}}

@article{dt_surrogate1,
	Author={Mark W. Craven and Jude W. Shavlik},
	Journal={Advances in Neural Information Processing Systems},
	Title={Extracting {T}ree-Structured {R}epresentations of {T}rained {N}etworks},
	note={URL: \url{http://papers.nips.cc/paper/1152-extracting-tree-structured-representations-of-trained-networks.pdf}},
	Year={1996}}

@article{been_kim1,
	Author={Finale Doshi-Velez and Been Kim},
	Title={Towards a {R}igorous {S}cience of {I}nterpretable {M}achine {L}earning},
	Journal={arXiv preprint arXiv:1702.08608},
	note={URL: \url{https://arxiv.org/pdf/1702.08608.pdf}},
	Year={2017}}

@inproceedings{feldman2015certifying,
  	title={Certifying and {R}emoving {D}isparate {I}mpact},
  	author={Feldman, Michael and Friedler, Sorelle A. and Moeller, John and Scheidegger, Carlos and Venkatasubramanian, Suresh},
  	booktitle={Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  	pages={259--268},
  	year={2015},
  	organization={ACM}, 
	note={\url{https://arxiv.org/pdf/1412.3756.pdf}}}

@book{esl,
	Address={New York},
	Author={Jerome Friedman and Trevor Hastie and Robert Tibshirani},
	Booktitle={\textit{The Elements of Statistical Learning}},
	Publisher={Springer},
	Title={\textbf{The Elements of Statistical Learning}},
	note={URL: \url{https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII\_print12.pdf}},
	Year={2001}}

@article{gilpin2018explaining,
	title={Explaining {E}xplanations: {A}n {A}pproach to {E}valuating {I}nterpretability of {M}achine {L}earning},
	author={Leilani H. Gilpin and David Bau and Ben Z. Yuan and Ayesha Bajwa and Michael Specter and Lalana Kagal},
	journal={arXiv preprint arXiv:1806.00069},
	note={URL: \url{https://arxiv.org/pdf/1806.00069.pdf}},
	year={2018}}

@article{ice_plots,
	Author={Alex Goldstein and Adam Kapelner and Justin Bleich and Emil Pitkin},
	Journal={Journal of Computational and Graphical Statistics},
	Number={1},
	Title={Peeking {I}nside the {B}lack {B}ox: {V}isualizing {S}tatistical {L}earning with {P}lots of {I}ndividual {C}onditional {E}xpectation},
	Volume={24},
	Year={2015},
	note={URL: \url{https://arxiv.org/pdf/1309.6392.pdf}}}

@article{guidotti2018survey,
 	title={A {S}urvey of {M}ethods for {E}xplaining {B}lack {B}ox {M}odels},
  	author={Guidotti, Riccardo and Monreale, Anna and Ruggieri, Salvatore and Turini, Franco and Giannotti, Fosca and Pedreschi, Dino},
  	journal={ACM Computing Surveys (CSUR)},
  	volume={51},
 	number={5},
  	pages={93},
  	year={2018},
  	publisher={ACM},
 	note={URL: \url{https://arxiv.org/pdf/1802.01933.pdf}}}

@book{h2o_mli_booklet,
	Author={Patrick Hall and Navdeep Gill and Megan Kurka and Wen Phan},
  	Year={2017},
  	Title={Machine Learning Interpretability with H2O Driverless AI},
  	note={URL: \url{http://docs.h2o.ai/driverless-ai/latest-stable/docs/booklets/MLIBooklet.pdf}},
  	Publisher = {H2O.ai}}

@article{lime-sup,
	title={Locally {I}nterpretable {M}odels and {E}ffects {B}ased on {S}upervised {P}artitioning ({LIME-SUP})},
	author={Linwei Hu and Jie Chen and Vijayan N. Nair and Agus Sudjianto},
	journal={arXiv preprint arXiv:1806.00663},
	note={URL: \url{https://arxiv.org/ftp/arxiv/papers/1806/1806.00663.pdf}},
	year={2018}}

@misc{uci ,
 	author="M. Lichman",
  	year="2013",
  	title="{UCI} {M}achine {L}earning {R}epository",
  	note="URL: \url{http://archive.ics.uci.edu/ml}",
  	institution="University of California, Irvine, School of Information and Computer Sciences"}

@article{lipton1,
 	title={The {M}ythos of {M}odel {I}nterpretability},
 	author={Lipton, Zachary C.},
 	journal={arXiv preprint arXiv:1606.03490},
 	year={2016},
 	note={URL: \url{https://arxiv.org/pdf/1606.03490.pdf}}}

@incollection{tree_shap,
	title={Consistent {I}ndividualized {F}eature {A}ttribution for {T}ree {E}nsembles},
	author={Lundberg, Scott M. and Erion, Gabriel G. and Lee, Su-In},
	booktitle={Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017)},
	pages={15--21},
	editor={Been Kim and Dmitry M. Malioutov and Kush R. Varshney and Adrian Weller},
	year={2017},
	note={URL: \url{https://openreview.net/pdf?id=ByTKSo-m-}}, 
	publisher={ICML WHI 2017},
	organization={ICML}}	

@incollection{shapley,
	title = {A {U}nified {A}pproach to {I}nterpreting {M}odel {P}redictions},
	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.},
	note={URL: \url{http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf}}}

@book{molnar,
  	title={Interpretable Machine Learning},
  	author={Christoph Molnar},
  	publisher={christophm.github.io/interpretable-ml-book},
  	note={URL: \url{https://christophm.github.io/interpretable-ml-book/}},
  	year={2018}}

@inproceedings{lime,
	title={Why {S}hould {I} {T}rust {Y}ou?: {E}xplaining the {P}redictions of {A}ny {C}lassifier},
	author={Marco Tulio Ribeiro and Sameer Singh and Carlos Guestrin},
	booktitle={Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
	pages={1135--1144},
	year={2016},
	note={URL: \url{http://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf}},
	organization={ACM}}

@article{please_stop,
 	title={Please Stop Explaining Black Box Models for High Stakes Decisions},
 	author={Rudin, Cynthia},
  	note={URL: \url{https://arxiv.org/pdf/1811.10154.pdf}},
  	journal={arXiv preprint arXiv:1811.10154},
  	year={2018}}

@misc{treeinterpreter,
 	title={Interpreting {R}andom {F}orests},
  	author={Saabas, Ando},
  	note={URL: \url{http://blog.datadive.net/interpreting-random-forests/}},
  	year={2014}}

@article{wf_xnn,
 	title={Explainable {N}eural {N}etworks {B}ased on {A}dditive {I}ndex {M}odels},
  	author={Vaughan, Joel and Sudjianto, Agus and Brahimi, Erind and Chen, Jie and Nair, Vijayan N},
  	journal={arXiv preprint arXiv:1806.01933},
  	note={URL: \url{https://arxiv.org/pdf/1806.01933.pdf}},
  	year={2018}}

@incollection{weller2017challenges,
    	title={Challenges for {T}ransparency},
    	author={Weller, Adrian},
	booktitle={Proceedings of the 2017 ICML Workshop on Human Interpretability in Machine Learning (WHI 2017)},
	pages={55-62},
	editor={Been Kim and Dmitry M. Malioutov and Kush R. Varshney and Adrian Weller},
	year={2017},
	note={URL: \url{https://openreview.net/pdf?id=SJR9L5MQ-}}, 
	publisher={ICML WHI 2017},
	organization={ICML}}	

@book{ff_interpretability,
  	author={Williams, Mike and others},
  	title={Interpretability},
  	publisher={Fast Forward Labs},
  	year={2017},
  	note={URL: \url{https://www.cloudera.com/products/fast-forward-labs-research.html}}}

@inproceedings{sbrl,
  	title={Scalable {B}ayesian {R}ule {L}ists},
  	author={Hongyu Yang and Cynthia Rudin and Margo Seltzer},
  	booktitle={Proceedings of the 34th International Conference on Machine Learning {(ICML)}},
  	note={URL: \url{https://arxiv.org/pdf/1602.08610.pdf}},
  	year={2017}}