{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T09:27:00Z","timestamp":1758274020108,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":60,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T00:00:00Z","timestamp":1585785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,4,2]]},"DOI":"10.1145\/3368555.3384460","type":"proceedings-article","created":{"date-parts":[[2020,3,20]],"date-time":"2020-03-20T20:37:37Z","timestamp":1584736657000},"page":"80-89","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Explaining an increase in predicted risk for clinical alerts"],"prefix":"10.1145","author":[{"given":"Michaela","family":"Hardt","sequence":"first","affiliation":[{"name":"Amazon"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alvin","family":"Rajkomar","sequence":"additional","affiliation":[{"name":"Google, UCSF"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gerardo","family":"Flores","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"Dai","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Howell","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Greg","family":"Corrado","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Claire","family":"Cui","sequence":"additional","affiliation":[{"name":"Google"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moritz","family":"Hardt","sequence":"additional","affiliation":[{"name":"UC Berkeley, Twitter"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,4,2]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_2_1_1_1","DOI":"10.1136\/bmjqs-2014-003499"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_2_1","DOI":"10.1377\/hlthaff.2014.0041"},{"key":"e_1_3_2_1_3_1","volume-title":"Early Detection of Impending Deterioration Outside The ICU: A Difference-in-Differences (DiD) Study","author":"Liu Vincent","year":"2016","unstructured":"Vincent Liu , Yan S. Kim , Benjamin J. Turk , Arona Ragins , Brian A. Dummett , Carnen L. Adams , Elizabeth A. Scruth , and Patricia Kipnis . Early Detection of Impending Deterioration Outside The ICU: A Difference-in-Differences (DiD) Study , pages A7614--A7614. 2016 . Vincent Liu, Yan S. Kim, Benjamin J. Turk, Arona Ragins, Brian A. Dummett, Carnen L. Adams, Elizabeth A. Scruth, and Patricia Kipnis. Early Detection of Impending Deterioration Outside The ICU: A Difference-in-Differences (DiD) Study, pages A7614--A7614. 2016."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_4_1","DOI":"10.1371\/journal.pone.0110274"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_5_1","DOI":"10.7326\/M17-2820"},{"key":"e_1_3_2_1_6_1","volume-title":"MLHC","author":"Tonekaboni Sana","year":"2019","unstructured":"Sana Tonekaboni , Shalmali Joshi , Melissa D. McCradden , and Anna Goldenberg . What clinicians want: Contextualizing explainable machine learning for clinical end use . In MLHC , 2019 . Sana Tonekaboni, Shalmali Joshi, Melissa D. McCradden, and Anna Goldenberg. What clinicians want: Contextualizing explainable machine learning for clinical end use. In MLHC, 2019."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_7_1","DOI":"10.5555\/1756006.1859912"},{"key":"e_1_3_2_1_9_1","volume-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps. CoRR, abs\/1312.6034","author":"Simonyan Karen","year":"2013","unstructured":"Karen Simonyan , Andrea Vedaldi , and Andrew Zisserman . Deep inside convolutional networks: Visualising image classification models and saliency maps. CoRR, abs\/1312.6034 , 2013 . Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. CoRR, abs\/1312.6034, 2013."},{"key":"e_1_3_2_1_10_1","volume-title":"ICML","author":"Sundararajan Mukund","year":"2017","unstructured":"Mukund Sundararajan , Ankur Taly , and Qiqi Yan . Axiomatic attribution for deep networks . In ICML , 2017 . Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep networks. In ICML, 2017."},{"key":"e_1_3_2_1_11_1","volume-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau Dzmitry","year":"2014","unstructured":"Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . Neural machine translation by jointly learning to align and translate . September 2014 . Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. September 2014."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_12_1","DOI":"10.1145\/2783258.2788613"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1016\/j.jbi.2013.06.011"},{"key":"e_1_3_2_1_14_1","volume-title":"Towards a rigorous science of interpretable machine learning. arXiv","author":"Doshi-Velez Finale","year":"2017","unstructured":"Finale Doshi-Velez and Been Kim . Towards a rigorous science of interpretable machine learning. arXiv , 2017 . Finale Doshi-Velez and Been Kim. Towards a rigorous science of interpretable machine learning. arXiv, 2017."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_15_1","DOI":"10.1007\/s11606-013-2376-6"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_16_1","DOI":"10.1097\/ACM.0000000000001148"},{"key":"e_1_3_2_1_17_1","volume-title":"Recurrent neural networks for multivariate time series with missing values. CoRR, abs\/1606.01865","author":"Che Zhengping","year":"2016","unstructured":"Zhengping Che , Sanjay Purushotham , Kyunghyun Cho , David Sontag , and Yan Liu . Recurrent neural networks for multivariate time series with missing values. CoRR, abs\/1606.01865 , 2016 . Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. Recurrent neural networks for multivariate time series with missing values. CoRR, abs\/1606.01865, 2016."},{"key":"e_1_3_2_1_18_1","volume-title":"MLHC","author":"Lipton Zachary C.","year":"2016","unstructured":"Zachary C. Lipton , David C. Kale , and Randall C. Wetzel . Directly modeling missing data in sequences with rnns: Improved classification of clinical time series . In MLHC , 2016 . Zachary C. Lipton, David C. Kale, and Randall C. Wetzel. Directly modeling missing data in sequences with rnns: Improved classification of clinical time series. In MLHC, 2016."},{"key":"e_1_3_2_1_19_1","volume-title":"Peter Szolovits, and Marzyeh Ghassemi. Clinical intervention prediction and understanding using deep networks.","author":"Suresh Harini","year":"2017","unstructured":"Harini Suresh , Nathan Hunt , Alistair Johnson , Leo Anthony Celi , Peter Szolovits, and Marzyeh Ghassemi. Clinical intervention prediction and understanding using deep networks. May 2017 . Harini Suresh, Nathan Hunt, Alistair Johnson, Leo Anthony Celi, Peter Szolovits, and Marzyeh Ghassemi. Clinical intervention prediction and understanding using deep networks. May 2017."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_20_1","DOI":"10.1136\/bmj.k1479"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_21_1","DOI":"10.2196\/jmir.9134"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_22_1","DOI":"10.1007\/s001340000677"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_23_1","DOI":"10.1109\/UIC-ATC.2017.8397411"},{"key":"e_1_3_2_1_24_1","volume-title":"ICML workshop on visualization for deep learning","author":"Smilkov D.","year":"2017","unstructured":"D. Smilkov , N. Thorat , B. Kim , F. Vi\u00e9gas , and M. Wattenberg . SmoothGrad: removing noise by adding noise . ICML workshop on visualization for deep learning , June 2017 . D. Smilkov, N. Thorat, B. Kim, F. Vi\u00e9gas, and M. Wattenberg. SmoothGrad: removing noise by adding noise. ICML workshop on visualization for deep learning, June 2017."},{"key":"e_1_3_2_1_25_1","first-page":"818","volume-title":"Computer Vision -- ECCV","author":"Matthew","year":"2014","unstructured":"Matthew D. Zeiler and Rob Fergus. Visualizing and understanding convolutional networks . In David Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars, editors, Computer Vision -- ECCV 2014 , pages 818 -- 833 , Cham, 2014. Springer International Publishing . Matthew D. Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. In David Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars, editors, Computer Vision -- ECCV 2014, pages 818--833, Cham, 2014. Springer International Publishing."},{"key":"e_1_3_2_1_26_1","volume-title":"Striving for simplicity: The all convolutional net. CoRR, abs\/1412.6806","author":"Springenberg Jost Tobias","year":"2014","unstructured":"Jost Tobias Springenberg , Alexey Dosovitskiy , Thomas Brox , and Martin A. Riedmiller . Striving for simplicity: The all convolutional net. CoRR, abs\/1412.6806 , 2014 . Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin A. Riedmiller. Striving for simplicity: The all convolutional net. CoRR, abs\/1412.6806, 2014."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_27_1","DOI":"10.18653\/v1\/P18-1176"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_28_1","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_1_29_1","first-page":"4765","volume-title":"Advances in Neural Information Processing Systems 30","author":"Lundberg Scott M","year":"2017","unstructured":"Scott M Lundberg and Su-In Lee . A unified approach to interpreting model predictions. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors , Advances in Neural Information Processing Systems 30 , pages 4765 -- 4774 . Curran Associates, Inc. , 2017 . Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 4765--4774. Curran Associates, Inc., 2017."},{"key":"e_1_3_2_1_30_1","first-page":"883","volume-title":"Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research","author":"Chen Jianbo","year":"2018","unstructured":"Jianbo Chen , Le Song , Martin Wainwright , and Michael Jordan . Learning to explain: An information-theoretic perspective on model interpretation. In Jennifer Dy and Andreas Krause, editors , Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research , pages 883 -- 892 , Stockholmsm\u00e4ssan, Stockholm Sweden, 10- -15 Jul 2018 . PMLR. Jianbo Chen, Le Song, Martin Wainwright, and Michael Jordan. Learning to explain: An information-theoretic perspective on model interpretation. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 883--892, Stockholmsm\u00e4ssan, Stockholm Sweden, 10--15 Jul 2018. PMLR."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_31_1","DOI":"10.1086\/511159"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_32_1","DOI":"10.1136\/bmjresp-2015-000091"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_33_1","DOI":"10.1097\/CCM.0000000000002105"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_34_1","DOI":"10.1097\/CCM.0b013e318259007b"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_35_1","DOI":"10.1377\/hlthaff.2015.0992"},{"key":"e_1_3_2_1_36_1","article-title":"A survey of recent advances in deep learning techniques for electronic health record (ehr) analysis","author":"Shickel Benjamin","year":"2017","unstructured":"Benjamin Shickel , Patrick Tighe , Azra Bihorac , and Parisa Rashidi . Deep ehr : A survey of recent advances in deep learning techniques for electronic health record (ehr) analysis . IEEE Journal of Biomedical and Health Informatics, PP(99):1--1 , 2017 . Benjamin Shickel, Patrick Tighe, Azra Bihorac, and Parisa Rashidi. Deep ehr: A survey of recent advances in deep learning techniques for electronic health record (ehr) analysis. IEEE Journal of Biomedical and Health Informatics, PP(99):1--1, 2017.","journal-title":"IEEE Journal of Biomedical and Health Informatics, PP(99):1--1"},{"key":"e_1_3_2_1_37_1","volume-title":"Proceedings of the 2nd Machine Learning for Healthcare Conference","volume":"68","author":"Suresh Harini","year":"2017","unstructured":"Harini Suresh , Nathan Hunt , Alistair Johnson , Leo Anthony Celi , Peter Szolovits , and Marzyeh Ghassemi . Clinical intervention prediction and understanding with deep neural networks. In Finale Doshi-Velez, Jim Fackler, David Kale, Rajesh Ranganath, Byron Wallace, and Jenna Wiens, editors , Proceedings of the 2nd Machine Learning for Healthcare Conference , volume 68 of Proceedings of Machine Learning Research, pages 322--337, Boston, Massachusetts, 18- -19 Aug 2017 . PMLR. Harini Suresh, Nathan Hunt, Alistair Johnson, Leo Anthony Celi, Peter Szolovits, and Marzyeh Ghassemi. Clinical intervention prediction and understanding with deep neural networks. In Finale Doshi-Velez, Jim Fackler, David Kale, Rajesh Ranganath, Byron Wallace, and Jenna Wiens, editors, Proceedings of the 2nd Machine Learning for Healthcare Conference, volume 68 of Proceedings of Machine Learning Research, pages 322--337, Boston, Massachusetts, 18--19 Aug 2017. PMLR."},{"key":"e_1_3_2_1_38_1","volume-title":"Deepr: A convolutional net for medical records","author":"Nguyen Phuoc","year":"2016","unstructured":"Phuoc Nguyen , Truyen Tran , Nilmini Wickramasinghe , and Svetha Venkatesh . Deepr: A convolutional net for medical records . July 2016 . Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, and Svetha Venkatesh. Deepr: A convolutional net for medical records. July 2016."},{"key":"e_1_3_2_1_39_1","first-page":"301","volume-title":"Proceedings of the 1st Machine Learning for Healthcare Conference","author":"Choi Edward","year":"2016","unstructured":"Edward Choi , Mohammad Taha Bahadori , Andy Schuetz , Walter F Stewart , and Jimeng Sun . Doctor AI : Predicting clinical events via recurrent neural networks . In Proceedings of the 1st Machine Learning for Healthcare Conference , pages 301 -- 318 . jmlr.org, 2016 . Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F Stewart, and Jimeng Sun. Doctor AI: Predicting clinical events via recurrent neural networks. In Proceedings of the 1st Machine Learning for Healthcare Conference, pages 301--318. jmlr.org, 2016."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_40_1","DOI":"10.1145\/2783258.2783365"},{"key":"e_1_3_2_1_41_1","volume-title":"Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. PLoS One, 8(6)","author":"Lasko Thomas A.","year":"2013","unstructured":"Thomas A. Lasko , Joshua C. Denny , and Mia A. Levy . Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. PLoS One, 8(6) , 2013 . Thomas A. Lasko, Joshua C. Denny, and Mia A. Levy. Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. PLoS One, 8(6), 2013."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_42_1","DOI":"10.1016\/j.jbi.2015.01.012"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_43_1","DOI":"10.1542\/peds.2013-0819"},{"issue":"1","key":"e_1_3_2_1_44_1","first-page":"47","article-title":"Learning temporal rules to forecast instability in continuously monitored patients","volume":"24","author":"Guillame-Bert Mathieu","year":"2017","unstructured":"Mathieu Guillame-Bert , Artur Dubrawski , Donghan Wang , Marilyn Hravnak , Gilles Clermont , and Michael R. Pinsky . Learning temporal rules to forecast instability in continuously monitored patients . JAMIA , 24 ( 1 ): 47 -- 53 , 2017 . Mathieu Guillame-Bert, Artur Dubrawski, Donghan Wang, Marilyn Hravnak, Gilles Clermont, and Michael R. Pinsky. Learning temporal rules to forecast instability in continuously monitored patients. JAMIA, 24(1):47--53, 2017.","journal-title":"JAMIA"},{"key":"e_1_3_2_1_45_1","volume-title":"Proceedings of the 1st Machine Learning for Healthcare Conference","author":"McCoy Roy Perlis Thomas","year":"2016","unstructured":"Thomas McCoy Roy Perlis Finale Doshi-Velez Michael C. Hughes , Huseyin Melih Elibol . Supervised topic models for clinical interpretability . In Proceedings of the 1st Machine Learning for Healthcare Conference , 2016 . Thomas McCoy Roy Perlis Finale Doshi-Velez Michael C. Hughes, Huseyin Melih Elibol. Supervised topic models for clinical interpretability. In Proceedings of the 1st Machine Learning for Healthcare Conference, 2016."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_46_1","DOI":"10.1109\/BIBM.2017.8217669"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_47_1","DOI":"10.1145\/3107411.3107445"},{"key":"e_1_3_2_1_48_1","first-page":"3504","volume-title":"Advances in Neural Information Processing Systems 29","author":"Choi Edward","year":"2016","unstructured":"Edward Choi , Mohammad Taha Bahadori , Jimeng Sun , Joshua Kulas , Andy Schuetz , and Walter Stewart . Retain : An interpretable predictive model for healthcare using reverse time attention mechanism. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors , Advances in Neural Information Processing Systems 29 , pages 3504 -- 3512 . Curran Associates, Inc. , 2016 . Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems 29, pages 3504--3512. Curran Associates, Inc., 2016."},{"key":"e_1_3_2_1_49_1","volume-title":"Interpretable deep models for icu outcome prediction. 2016:371--380, 02","author":"Che Zhengping","year":"2017","unstructured":"Zhengping Che , Sanjay Purushotham , Robinder Khemani , and Yan Liu . Interpretable deep models for icu outcome prediction. 2016:371--380, 02 2017 . Zhengping Che, Sanjay Purushotham, Robinder Khemani, and Yan Liu. Interpretable deep models for icu outcome prediction. 2016:371--380, 02 2017."},{"key":"e_1_3_2_1_50_1","volume-title":"NIPS Workshop on Machine Learning for Healthcare","author":"Che Zhengping","year":"2015","unstructured":"Zhengping Che , Sanjay Purushotham , Robinder G. Khemani , and Yan Liu . Distilling knowledge from deep networks with applications to healthcare domain . In NIPS Workshop on Machine Learning for Healthcare , 2015 . Zhengping Che, Sanjay Purushotham, Robinder G. Khemani, and Yan Liu. Distilling knowledge from deep networks with applications to healthcare domain. In NIPS Workshop on Machine Learning for Healthcare, 2015."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_51_1","DOI":"10.1109\/TIP.2004.823819"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_52_1","DOI":"10.1162\/neco.1997.9.8.1735"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_53_1","DOI":"10.1038\/sdata.2016.35"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_54_1","DOI":"10.1016\/j.jacc.2007.02.067"},{"key":"e_1_3_2_1_55_1","volume-title":"Scalable and accurate deep learning for electronic health records. npj Digital Medicine","author":"Rajkomar Alvin","year":"2018","unstructured":"Alvin Rajkomar , Eyal Oren , Kai Chen , Andrew M Dai , Nissan Hajaj , Peter J Liu , Xiaobing Liu , Mimi Sun , Patrik Sundberg , Hector Yee , Kun Zhang , Gavin E Duggan , Gerardo Flores , Michaela Hardt , Jamie Irvine , Quoc Le , Kurt Litsch , Jake Marcus , Alexander Mossin , Justin Tansuwan , De Wang , James Wexler , Jimbo Wilson , Dana Ludwig , Samuel L Volchenboum , Katherine Chou , Michael Pearson , Srinivasan Madabushi , Nigam H Shah , Atul J Butte , Michael Howell , Claire Cui , Greg Corrado , and Jeff Dean . Scalable and accurate deep learning for electronic health records. npj Digital Medicine , 1, January 2018 . Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M Dai, Nissan Hajaj, Peter J Liu, Xiaobing Liu, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Gavin E Duggan, Gerardo Flores, Michaela Hardt, Jamie Irvine, Quoc Le, Kurt Litsch, Jake Marcus, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H Shah, Atul J Butte, Michael Howell, Claire Cui, Greg Corrado, and Jeff Dean. Scalable and accurate deep learning for electronic health records. npj Digital Medicine, 1, January 2018."},{"key":"e_1_3_2_1_56_1","first-page":"1019","volume-title":"Advances in Neural Information Processing Systems 29","author":"Gal Yarin","year":"2016","unstructured":"Yarin Gal and Zoubin Ghahramani . A theoretically grounded application of dropout in recurrent neural networks. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors , Advances in Neural Information Processing Systems 29 , pages 1019 -- 1027 . Curran Associates, Inc. , 2016 . Yarin Gal and Zoubin Ghahramani. A theoretically grounded application of dropout in recurrent neural networks. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems 29, pages 1019--1027. Curran Associates, Inc., 2016."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_57_1","DOI":"10.5555\/1953048.2021068"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_58_1","DOI":"10.1186\/cc11454"},{"key":"e_1_3_2_1_59_1","volume-title":"NIPS workshop on Explaining and Visualizing Deep Learning","author":"Kindermans P.-J.","year":"2017","unstructured":"P.-J. Kindermans , S. Hooker , J. Adebayo , M. Alber , K. T. Sch\u00fctt , S. D\u00e4hne , D. Erhan , and B. Kim . The (Un)reliability of saliency methods . NIPS workshop on Explaining and Visualizing Deep Learning , 2017 . P.-J. Kindermans, S. Hooker, J. Adebayo, M. Alber, K. T. Sch\u00fctt, S. D\u00e4hne, D. Erhan, and B. Kim. The (Un)reliability of saliency methods. NIPS workshop on Explaining and Visualizing Deep Learning, 2017."},{"key":"e_1_3_2_1_60_1","volume-title":"ICML Workshop on Human Interpretability","author":"Lipton Zachary Chase","year":"2016","unstructured":"Zachary Chase Lipton . The mythos of model interpretability . In ICML Workshop on Human Interpretability , 2016 . Zachary Chase Lipton. The mythos of model interpretability. In ICML Workshop on Human Interpretability, 2016."},{"key":"e_1_3_2_1_61_1","volume-title":"Dice in the black box: User experiences with an inscrutable algorithm","author":"Springer Aaron","year":"2017","unstructured":"Aaron Springer , Victoria Hollis , and Steve Whittaker . Dice in the black box: User experiences with an inscrutable algorithm , 2017 . Aaron Springer, Victoria Hollis, and Steve Whittaker. Dice in the black box: User experiences with an inscrutable algorithm, 2017."}],"event":{"sponsor":["ACM Association for Computing Machinery"],"acronym":"ACM CHIL '20","name":"ACM CHIL '20: ACM Conference on Health, Inference, and Learning","location":"Toronto Ontario Canada"},"container-title":["Proceedings of the ACM Conference on Health, Inference, and Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3368555.3384460","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3368555.3384460","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:01:26Z","timestamp":1750197686000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3368555.3384460"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,2]]},"references-count":60,"alternative-id":["10.1145\/3368555.3384460","10.1145\/3368555"],"URL":"https:\/\/doi.org\/10.1145\/3368555.3384460","relation":{},"subject":[],"published":{"date-parts":[[2020,4,2]]},"assertion":[{"value":"2020-04-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}