{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T06:19:34Z","timestamp":1782281974877,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":63,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T00:00:00Z","timestamp":1580083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,1,27]]},"DOI":"10.1145\/3351095.3372827","type":"proceedings-article","created":{"date-parts":[[2020,1,23]],"date-time":"2020-01-23T13:43:02Z","timestamp":1579786982000},"page":"99-109","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":163,"title":["\"The human body is a black box\""],"prefix":"10.1145","author":[{"given":"Mark","family":"Sendak","sequence":"first","affiliation":[{"name":"Duke Institute for Health Innovation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madeleine Clare","family":"Elish","sequence":"additional","affiliation":[{"name":"Data & Society Research Institute"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Gao","sequence":"additional","affiliation":[{"name":"Duke Institute for Health Innovation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph","family":"Futoma","sequence":"additional","affiliation":[{"name":"Harvard University and Duke University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Ratliff","sequence":"additional","affiliation":[{"name":"Duke Institute for Health Innovation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marshall","family":"Nichols","sequence":"additional","affiliation":[{"name":"Duke Institute for Health Innovation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Armando","family":"Bedoya","sequence":"additional","affiliation":[{"name":"Duke School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suresh","family":"Balu","sequence":"additional","affiliation":[{"name":"Duke Institute for Health Innovation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cara","family":"O'Brien","sequence":"additional","affiliation":[{"name":"Duke School of Medicine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,1,27]]},"reference":[{"key":"e_1_3_2_1_1_1","article-title":"Patient Outcomes and Cost-Effectiveness of a Sepsis Care Quality Improvement Program in a Health System","author":"Afshar M.","year":"2019","unstructured":"Afshar , M. , Arain , E. , Ye , C. , Gilbert , E. , Xie , M. , Lee , J. , Churpek , M.M. , Durazo-Arvizu , R. , Markossian , T. and Joyce , C. 2019 . Patient Outcomes and Cost-Effectiveness of a Sepsis Care Quality Improvement Program in a Health System . Journal of the American Medical Association. ( Jul. 2019). Afshar, M., Arain, E., Ye, C., Gilbert, E., Xie, M., Lee, J., Churpek, M.M., Durazo-Arvizu, R., Markossian, T. and Joyce, C. 2019. Patient Outcomes and Cost-Effectiveness of a Sepsis Care Quality Improvement Program in a Health System. Journal of the American Medical Association. (Jul. 2019).","journal-title":"Journal of the American Medical Association."},{"key":"e_1_3_2_1_2_1","volume-title":"IEEE Intelligent Informatice Bulletin. 19","author":"Ahmad M.A.","year":"2018","unstructured":"Ahmad , M.A. , Eckert , C. , Teredesia , A. and McKelvey , G. 2018 . Interpretable Machine Learning in Healthcare . IEEE Intelligent Informatice Bulletin. 19 , (Aug. 2018), 1--7. Ahmad, M.A., Eckert, C., Teredesia, A. and McKelvey, G. 2018. Interpretable Machine Learning in Healthcare. IEEE Intelligent Informatice Bulletin. 19, (Aug. 2018), 1--7."},{"key":"e_1_3_2_1_3_1","first-page":"3","article-title":"A Framework for the Development and Interpretation of Different Sepsis Definitions and Clinical Criteria","volume":"44","author":"Angus D.C.","year":"2016","unstructured":"Angus , D.C. , Seymour , C.W. , Coopersmith , C.M. , Deutschman , C.S. , Klompas , M. , Levy , M.M. , Martin , G.S. , Osborn , T.M. , Rhee , C. and Watson , R.S. 2016 . A Framework for the Development and Interpretation of Different Sepsis Definitions and Clinical Criteria . Journal of the American Medical Association. 44 , 3 (Mar. 2016), e113--e121. Angus, D.C., Seymour, C.W., Coopersmith, C.M., Deutschman, C.S., Klompas, M., Levy, M.M., Martin, G.S., Osborn, T.M., Rhee, C. and Watson, R.S. 2016. A Framework for the Development and Interpretation of Different Sepsis Definitions and Clinical Criteria. Journal of the American Medical Association. 44, 3 (Mar. 2016), e113--e121.","journal-title":"Journal of the American Medical Association."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000003439"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.289.15.1969"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"boyd D. and Crawford K. 2012. Critical Questions for Big Data. Information Ccommunication and Society. 5 (May 2012) 662--679.  boyd D. and Crawford K. 2012. Critical Questions for Big Data. Information Ccommunication and Society. 5 (May 2012) 662--679.","DOI":"10.1080\/1369118X.2012.678878"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1177\/2053951715622512"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2788613"},{"key":"e_1_3_2_1_9_1","first-page":"4","article-title":"Using recurrent neural network models for early detection of heart failure onset","volume":"61","author":"Choi E.","year":"2016","unstructured":"Choi , E. , Schuetz , A. , Stewart , W.F. and Sun , J. 2016 . Using recurrent neural network models for early detection of heart failure onset . Journal of the American Medical Informatics Association. 61 , 4 (Aug. 2016), ocw112--10. Choi, E., Schuetz, A., Stewart, W.F. and Sun, J. 2016. Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association. 61, 4 (Aug. 2016), ocw112--10.","journal-title":"Journal of the American Medical Informatics Association."},{"key":"e_1_3_2_1_10_1","unstructured":"Citron D.K. and Pasquale F. 2014. The Scored Society: Due Proccess for Automated Predictions. Washington Law Review. (Mar. 2014).  Citron D.K. and Pasquale F. 2014. The Scored Society: Due Proccess for Automated Predictions. Washington Law Review. (Mar. 2014)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.7326\/M14-0698"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocx030"},{"key":"e_1_3_2_1_13_1","unstructured":"Doshi-Valez F. and Kim B. 2017. Towards a Rigorous Science of Interpretable Machine Learning. (Mar. 2017) 1--13.  Doshi-Valez F. and Kim B. 2017. Towards a Rigorous Science of Interpretable Machine Learning. (Mar. 2017) 1--13."},{"key":"e_1_3_2_1_14_1","unstructured":"Downing N.L. Rolnick J. Poole S.F. Hall E. Wessels A.J. Heidenreich P. and Shieh L. 2019. Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation. BMJ Quality & Safety. (Mar. 2019) bmjqs-2018-008765-7.  Downing N.L. Rolnick J. Poole S.F. Hall E. Wessels A.J. Heidenreich P. and Shieh L. 2019. Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation. BMJ Quality & Safety. (Mar. 2019) bmjqs-2018-008765-7."},{"key":"e_1_3_2_1_15_1","volume-title":"The Stakes of Uncertainty: Developing and Integrating Machine Learning in Clinical Care. EPIC Proceedings. (Nov.","author":"Elish M.C.","year":"2018","unstructured":"Elish , M.C. 2018 . The Stakes of Uncertainty: Developing and Integrating Machine Learning in Clinical Care. EPIC Proceedings. (Nov. 2018). Elish, M.C. 2018. The Stakes of Uncertainty: Developing and Integrating Machine Learning in Clinical Care. EPIC Proceedings. (Nov. 2018)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2018.7565"},{"key":"e_1_3_2_1_17_1","unstructured":"Ferryman K. and Pitcan M. 2018. Fairness in precision medicine. Data & Society. 2018. Data & Society.  Ferryman K. and Pitcan M. 2018. Fairness in precision medicine. Data & Society. 2018. Data & Society."},{"key":"e_1_3_2_1_18_1","volume-title":"International Conference on Machine Learning. (Jun.","author":"Futoma J.","year":"2017","unstructured":"Futoma , J. , Hariharan , S. and Heller , K . 2017. Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier . International Conference on Machine Learning. (Jun. 2017 ), 1--9. Futoma, J., Hariharan, S. and Heller, K. 2017. Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier. International Conference on Machine Learning. (Jun. 2017), 1--9."},{"key":"e_1_3_2_1_19_1","volume-title":"An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection. Proceedings of Machine Learning for Healthcare. (Aug.","author":"Futoma J.","year":"2017","unstructured":"Futoma , J. , Hariharan , S. , Heller , K. , Sendak , M.P. , Brajer , N. , Clement , M. , Bedoya , A. and O Brien , C. 2017 . An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection. Proceedings of Machine Learning for Healthcare. (Aug. 2017). Futoma, J., Hariharan, S., Heller, K., Sendak, M.P., Brajer, N., Clement, M., Bedoya, A. and OBrien, C. 2017. An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection. Proceedings of Machine Learning for Healthcare. (Aug. 2017)."},{"key":"e_1_3_2_1_20_1","volume-title":"The Velluvial Matrix. The New Yorker. (Jan","author":"Gawande A.","year":"2010","unstructured":"Gawande , A. 2010. The Velluvial Matrix. The New Yorker. (Jan . 2010 ). Gawande, A. 2010. The Velluvial Matrix. The New Yorker. (Jan. 2010)."},{"key":"e_1_3_2_1_21_1","volume-title":"Disparate Interactions.","author":"Green B.","year":"2019","unstructured":"Green , B. and Chen , Y . 2019 . Disparate Interactions. ( New York, New York, USA , 2019 ), 90--99. Green, B. and Chen, Y. 2019. Disparate Interactions. (New York, New York, USA, 2019), 90--99."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.8775"},{"key":"e_1_3_2_1_23_1","volume-title":"Artificial Professional Advice. Yale Journal of Law Technology. (Jun","author":"Haupt C.E.","year":"2019","unstructured":"Haupt , C.E. 2019. Artificial Professional Advice. Yale Journal of Law Technology. (Jun . 2019 ). Haupt, C.E. 2019. Artificial Professional Advice. Yale Journal of Law Technology. (Jun. 2019)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"He J. Baxter S.L. Xu J. Xu J. Zhou X. and Zhang K. 2019. The practical implementation of artificial intelligence technologies in medicine. Nature medicine. 25 1 (Jan. 2019) 30--36.  He J. Baxter S.L. Xu J. Xu J. Zhou X. and Zhang K. 2019. The practical implementation of artificial intelligence technologies in medicine. Nature medicine. 25 1 (Jan. 2019) 30--36.","DOI":"10.1038\/s41591-018-0307-0"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2009.07.002"},{"key":"e_1_3_2_1_26_1","volume-title":"Dihital Tornado: Networks, Algorithms, Humanity","author":"Kluttz D.N.","year":"2020","unstructured":"Kluttz , D.N. , Kohli , N. and Mulligan , D.K . 2020 . Shaping Our Tools: Contestability as a Means to Promote Responsible Algorithmic Decision Making in the Professions, in After the Dihital Tornado: Networks, Algorithms, Humanity . Combridge University Press . (2020). Kluttz, D.N., Kohli, N. and Mulligan, D.K. 2020. Shaping Our Tools: Contestability as a Means to Promote Responsible Algorithmic Decision Making in the Professions, in After the Dihital Tornado: Networks, Algorithms, Humanity. Combridge University Press. (2020)."},{"key":"e_1_3_2_1_27_1","volume-title":"To Err is Human: Building a Safer Health System","author":"Kohn L.T.","year":"1999","unstructured":"Kohn , L.T. , Corrihan , J.M. , Donaldson , M.S. eds 1999. To Err is Human: Building a Safer Health System . National Academy Press , Institute of Medicine. ( 1999 ). Kohn, L.T., Corrihan, J.M., Donaldson, M.S.eds 1999. To Err is Human: Building a Safer Health System. National Academy Press, Institute of Medicine. (1999)."},{"key":"e_1_3_2_1_28_1","volume-title":"Leading Change: Why Transformation Efforts Fail. Harvard business review. (Mar.","author":"Kotter J.P.","year":"1995","unstructured":"Kotter , J.P. 1995 . Leading Change: Why Transformation Efforts Fail. Harvard business review. (Mar. 1995), 59--67. Kotter, J.P. 1995. Leading Change: Why Transformation Efforts Fail. Harvard business review. (Mar. 1995), 59--67."},{"key":"e_1_3_2_1_29_1","first-page":"633","article-title":"Accountable algorithms","volume":"165","author":"Kroll J.A.","year":"2017","unstructured":"Kroll , J.A. , Huey , J. , Barocas , S. , Felten , E.W. , Reidenberg , J.R. , Robinson , D.G. and Yu , H. 2017 . Accountable algorithms . University of Pennsylvania Law Review. 165 , 3 (2017), 633 -- 705 . Kroll, J.A., Huey, J., Barocas, S., Felten, E.W., Reidenberg, J.R., Robinson, D.G. and Yu, H. 2017. Accountable algorithms. University of Pennsylvania Law Review. 165, 3 (2017), 633--705.","journal-title":"University of Pennsylvania Law Review."},{"key":"e_1_3_2_1_30_1","volume-title":"What Is Sepsis: Investigating the Heterogeneity of Patient Populations Captured by Different Sepsis Definitions. American Journal of Respiratory and Critical Care Medicine. (May","author":"Lin A.L.","year":"2018","unstructured":"Lin , A.L. , Sendak , M.P. , Bedoya , A.D. , Clement , M.E. , Futoma , J. , Nichols , M. , Gao , M. , Heller , K. and O Brien , C. 2018. What Is Sepsis: Investigating the Heterogeneity of Patient Populations Captured by Different Sepsis Definitions. American Journal of Respiratory and Critical Care Medicine. (May 2018 ). Lin, A.L., Sendak, M.P., Bedoya, A.D., Clement, M.E., Futoma, J., Nichols, M., Gao, M., Heller, K. and OBrien, C. 2018. What Is Sepsis: Investigating the Heterogeneity of Patient Populations Captured by Different Sepsis Definitions. American Journal of Respiratory and Critical Care Medicine. (May 2018)."},{"key":"e_1_3_2_1_31_1","volume-title":"Interpretable ML Symposium NeurIPS 2017","author":"Lipton Z.C.","year":"2017","unstructured":"Lipton , Z.C. 2017 . The Doctor Just Won't Accept That . Interpretable ML Symposium NeurIPS 2017 . (Nov. 2017). Lipton, Z.C. 2017. The Doctor Just Won't Accept That. Interpretable ML Symposium NeurIPS 2017. (Nov. 2017)."},{"key":"e_1_3_2_1_32_1","volume-title":"The Mythos of Model Interpretability. ICML Workshop on Human Interpretability.","author":"Lipton Z.C.","year":"2016","unstructured":"Lipton , Z.C. 2016 . The Mythos of Model Interpretability. ICML Workshop on Human Interpretability. (2016). Lipton, Z.C. 2016. The Mythos of Model Interpretability. ICML Workshop on Human Interpretability. (2016)."},{"key":"e_1_3_2_1_33_1","volume-title":"Proceedings of Machine Learning for Healthcare.","author":"Lipton Z.C.","year":"2016","unstructured":"Lipton , Z.C. , Kale , D.C. and Wetzel , R . 2016. Modeling missing data in clinical time series with rnns . Proceedings of Machine Learning for Healthcare. ( 2016 ). Lipton, Z.C., Kale, D.C. and Wetzel, R. 2016. Modeling missing data in clinical time series with rnns. Proceedings of Machine Learning for Healthcare. (2016)."},{"key":"e_1_3_2_1_34_1","volume-title":"Context: The Labor of Integrating New Technologies","author":"Mateescu A.","year":"2019","unstructured":"Mateescu , A. and Elish , M.C . 2019 . AI in Context: The Labor of Integrating New Technologies . Data & Society . (Jan. 2019), 1--54. Mateescu, A. and Elish, M.C. 2019. AI in Context: The Labor of Integrating New Technologies. Data & Society. (Jan. 2019), 1--54."},{"key":"e_1_3_2_1_35_1","volume-title":"Explanation in artificial intelligence: Insights from the social sciences. 267, (Jun","author":"Miller","year":"2017","unstructured":"Miller , Tim 2017. Explanation in artificial intelligence: Insights from the social sciences. 267, (Jun . 2017 ), 1--38. Miller, Tim 2017. Explanation in artificial intelligence: Insights from the social sciences. 267, (Jun. 2017), 1--38."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287596"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287574"},{"key":"e_1_3_2_1_38_1","volume-title":"not until you sign away your privacy","author":"Need","year":"2018","unstructured":"Need medical help? Sorry , not until you sign away your privacy : 2018 . https:\/\/www.technologyreview.com\/s\/612282\/need-medical-help-sorry-not-until-you-sign-away-your-privacy\/. Accessed : 2019-08-23. Need medical help? Sorry, not until you sign away your privacy: 2018. https:\/\/www.technologyreview.com\/s\/612282\/need-medical-help-sorry-not-until-you-sign-away-your-privacy\/. Accessed: 2019-08-23."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Neff G. Tanweer A. Fiore-Gartland B. and Osburn L. 2017. Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science. Big data. 5 2 (Jun. 2017) 85--97.  Neff G. Tanweer A. Fiore-Gartland B. and Osburn L. 2017. Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science. Big data. 5 2 (Jun. 2017) 85--97.","DOI":"10.1089\/big.2016.0050"},{"key":"e_1_3_2_1_40_1","volume-title":"Machine Learning for Health MLH Workshop at NeurIPS. (Nov.","author":"Nestor B.","year":"2018","unstructured":"Nestor , B. , McDermott , M.B. , Chauhau , G. , Naumann , T. , Hughes , M.C. , Goldenberg , A. and Ghassemi , M . 2018. Rethinking Clinical Prediction: Why Machine learning Must Consider Year of Care and Feature Aggregation . Machine Learning for Health MLH Workshop at NeurIPS. (Nov. 2018 ). Nestor, B., McDermott, M.B., Chauhau, G., Naumann, T., Hughes, M.C., Goldenberg, A. and Ghassemi, M. 2018. Rethinking Clinical Prediction: Why Machine learning Must Consider Year of Care and Feature Aggregation. Machine Learning for Health MLH Workshop at NeurIPS. (Nov. 2018)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aax2342"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1017\/ice.2018.16"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287567"},{"key":"e_1_3_2_1_44_1","volume-title":"Regulating Black-Box Medicine. Michigan law review. 116, 3","author":"Price W.N.","year":"2017","unstructured":"Price , W.N. 2017. Regulating Black-Box Medicine. Michigan law review. 116, 3 ( 2017 ), 421--474. Price, W.N. 2017. Regulating Black-Box Medicine. Michigan law review. 116, 3 (2017), 421--474."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1814259"},{"key":"e_1_3_2_1_46_1","first-page":"10","article-title":"Compliance With the National SEP-1 Quality Measure and Association With Sepsis Outcomes","volume":"46","author":"Rhee C.","year":"2018","unstructured":"Rhee , C. 2018 . Compliance With the National SEP-1 Quality Measure and Association With Sepsis Outcomes . Journal of the American Medical Association. 46 , 10 (Oct. 2018), 1585--1591. Rhee, C. et al. 2018. Compliance With the National SEP-1 Quality Measure and Association With Sepsis Outcomes. Journal of the American Medical Association. 46, 10 (Oct. 2018), 1585--1591.","journal-title":"Journal of the American Medical Association."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.13836"},{"key":"e_1_3_2_1_48_1","volume-title":"Diffusion of Innovations","author":"Rogers E.M.","unstructured":"Rogers , E.M. 1995. Diffusion of Innovations . The Free Press . Rogers, E.M. 1995. Diffusion of Innovations. The Free Press."},{"key":"e_1_3_2_1_49_1","volume-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence. (May","author":"Rudin C.","year":"2019","unstructured":"Rudin , C. 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence. (May 2019 ), 206--215. Rudin, C. 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence. (May 2019), 206--215."},{"key":"e_1_3_2_1_50_1","volume-title":"Tutorial: Safe and Reliable Machine Learning. ACM Conference on Fairness, Accountability, and Transparency. (Apr.","author":"Saria S.","year":"2019","unstructured":"Saria , S. and Subbaswamy , A . 2019 . Tutorial: Safe and Reliable Machine Learning. ACM Conference on Fairness, Accountability, and Transparency. (Apr. 2019 ), 1--4. Saria, S. and Subbaswamy, A. 2019. Tutorial: Safe and Reliable Machine Learning. ACM Conference on Fairness, Accountability, and Transparency. (Apr. 2019), 1--4."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Selbst A.D. and Barocas S. 2018. The intuitive appeal of explainable machines. Fordham Law Review. (2018) 1085--1139.  Selbst A.D. and Barocas S. 2018. The intuitive appeal of explainable machines. Fordham Law Review. (2018) 1085--1139.","DOI":"10.2139\/ssrn.3126971"},{"key":"e_1_3_2_1_52_1","volume-title":"The National Artificial Intelligence Research and Development Strategic Plan: 2019 Update","author":"Select Committee on Artificial Intelligence 2019.","year":"2019","unstructured":"Select Committee on Artificial Intelligence 2019. The National Artificial Intelligence Research and Development Strategic Plan: 2019 Update . National Science Technology Council . ( Jun. 2019 ). Select Committee on Artificial Intelligence 2019. The National Artificial Intelligence Research and Development Strategic Plan: 2019 Update. National Science Technology Council. (Jun. 2019)."},{"key":"e_1_3_2_1_53_1","volume-title":"et al","author":"Sendak M.P.","year":"2019","unstructured":"Sendak , M.P. et al . 2019 . Sepsis Watch : A Real-World Integration of Deep Learning into Routine Clinical Care. JMIR Preprints . (Jun. 2019). Sendak, M.P. et al. 2019. Sepsis Watch: A Real-World Integration of Deep Learning into Routine Clinical Care. JMIR Preprints. (Jun. 2019)."},{"key":"e_1_3_2_1_54_1","volume-title":"Health Care: A Critical Appraisal of Challenges and Opportunities. eGEMs Generating Evidence and Methods to Improve Patient Outcomes. 7, 1 (Jan.","author":"Sendak M.P.","year":"2019","unstructured":"Sendak , M.P. , Gao , M. , Nichols , M. , Lin , A. and Balu , S . 2019 . Machine Learning in Health Care: A Critical Appraisal of Challenges and Opportunities. eGEMs Generating Evidence and Methods to Improve Patient Outcomes. 7, 1 (Jan. 2019), 1--4. Sendak, M.P., Gao, M., Nichols, M., Lin, A. and Balu, S. 2019. Machine Learning in Health Care: A Critical Appraisal of Challenges and Opportunities. eGEMs Generating Evidence and Methods to Improve Patient Outcomes. 7, 1 (Jan. 2019), 1--4."},{"key":"e_1_3_2_1_55_1","volume-title":"et al","author":"Seymour C.W.","year":"2019","unstructured":"Seymour , C.W. et al . 2019 . Derivation, Validation , and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis. JAMA. (May 2019), 1--15. Seymour, C.W. et al. 2019. Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis. JAMA. (May 2019), 1--15."},{"key":"e_1_3_2_1_56_1","unstructured":"Seymour C.W. Gesten F. Prescott H.C. Friedrich M.E. Iwashyna T.J. Phillips G.S. Lemeshow S. Osborn T. Terry K.M. and Levy M.M. 2017. Time to Treatment and Mortality during Mandated Emergency Care for Sepsis. New England Journal of Medicine. (May 2017) NEJMoa1703058--10.  Seymour C.W. Gesten F. Prescott H.C. Friedrich M.E. Iwashyna T.J. Phillips G.S. Lemeshow S. Osborn T. Terry K.M. and Levy M.M. 2017. Time to Treatment and Mortality during Mandated Emergency Care for Sepsis. New England Journal of Medicine. (May 2017) NEJMoa1703058--10."},{"key":"e_1_3_2_1_57_1","unstructured":"Strauss A.L. and Corbin J.M. 1997. Grounded Theory in Practice. Sage.  Strauss A.L. and Corbin J.M. 1997. Grounded Theory in Practice. Sage."},{"key":"e_1_3_2_1_58_1","volume-title":"Basic Books","author":"Topol E.J.","year":"2019","unstructured":"Topol , E.J. Deep Medicine (p. 18). Basic Books . March 2019 . Topol, E.J. Deep Medicine (p. 18). Basic Books. March 2019."},{"key":"e_1_3_2_1_59_1","volume-title":"High-performance medicine: the convergence of human and artificial intelligence. Nature medicine. 25, 1 (Jan","author":"Topol E.J.","year":"2019","unstructured":"Topol , E.J. 2019. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine. 25, 1 (Jan . 2019 ), 44--56. Topol, E.J. 2019. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine. 25, 1 (Jan. 2019), 44--56."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.19198"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"crossref","unstructured":"Wachter S. Mittelstadt B. and Floridi L. 2017. Why a right to explanation of automated decision-making does not exist in the general data protection regulation. International Data Privacy Law. 7 (2017).  Wachter S. Mittelstadt B. and Floridi L. 2017. Why a right to explanation of automated decision-making does not exist in the general data protection regulation. International Data Privacy Law. 7 (2017).","DOI":"10.1093\/idpl\/ipx005"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1136\/amiajnl-2013-002162"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002683"}],"event":{"name":"FAT* '20: Conference on Fairness, Accountability, and Transparency","location":"Barcelona Spain","acronym":"FAT* '20","sponsor":["ACM Association for Computing Machinery"]},"container-title":["Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3351095.3372827","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3351095.3372827","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:41:22Z","timestamp":1750200082000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3351095.3372827"}},"subtitle":["supporting clinical decision-making with deep learning"],"short-title":[],"issued":{"date-parts":[[2020,1,27]]},"references-count":63,"alternative-id":["10.1145\/3351095.3372827","10.1145\/3351095"],"URL":"https:\/\/doi.org\/10.1145\/3351095.3372827","relation":{},"subject":[],"published":{"date-parts":[[2020,1,27]]},"assertion":[{"value":"2020-01-27","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}