{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T16:50:22Z","timestamp":1777222222569,"version":"3.51.4"},"reference-count":65,"publisher":"MIS Quarterly","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,1]]},"abstract":"<jats:p>Recent developments in big data technologies are revolutionizing the field of healthcare predictive analytics (HPA), enabling researchers to explore challenging problems using complex prediction models. Nevertheless, healthcare practitioners are reluctant to adopt those models as they are less transparent and accountable due to their black-box structure. We believe that instance-level, or local, explanations enhance patient safety and foster trust by enabling patient-level interpretations and medical knowledge discovery. Therefore, we propose the RObust Local EXplanations (ROLEX) method to develop robust, instance-level explanations for HPA models in this study. ROLEX adapts state-of-the-art methods and ameliorates their shortcomings in explaining individual-level predictions made by black-box machine learning models. Our analysis with a large real-world dataset related to a prevalent medical condition called fragility fracture and two publicly available healthcare datasets reveals that ROLEX outperforms widely accepted benchmark methods in terms of local faithfulness of explanations. In addition, ROLEX is more robust since it does not rely on extensive hyperparameter tuning or heuristic algorithms. Explanations generated by ROLEX, along with the prototype user interface presented in this study, have the potential to promote personalized care and precision medicine by providing patient-level interpretations and novel insights. We discuss the theoretical implications of our study in healthcare, big data, and design science.<\/jats:p>","DOI":"10.25300\/misq\/2022\/17141","type":"journal-article","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T17:14:55Z","timestamp":1696007695000},"page":"1303-1332","source":"Crossref","is-referenced-by-count":26,"title":["ROLEX: A Novel Method for Interpretable Machine Learning Using Robust Local Explanations"],"prefix":"10.25300","volume":"47","author":[{"given":"Buomsoo (Raymond)","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Information Systems and Business Analytics, Iowa State University Ames, IA, U.S.A."}]},{"given":"Karthik","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"School of Business, University of Kansas Lawrence, KS, U.S.A."}]},{"given":"Sung Hye","family":"Kong","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Seoul National University Hospital Seoul, Republic of Korea"}]},{"given":"Jung Hee","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Seoul National University Hospital Seoul, Republic of Korea"}]},{"given":"Chan Soo","family":"Shin","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Seoul National University Hospital Seoul, Republic of Korea"}]},{"given":"Sudha","family":"Ram","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, University of Arizona Tucson, AZ, U.S.A."}]}],"member":"10933","published-online":{"date-parts":[[2023,9,1]]},"reference":[{"key":"2025082212311716600_b1-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1109\/ICHI.2018.00095","article-title":"Interpretable machine learning in healthcare","author":"Ahmad","year":"2018"},{"key":"2025082212311716600_b2-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673","article-title":"Intrinsic dimensionality estimation within tight localities","author":"Amsaleg","year":"2019"},{"key":"2025082212311716600_b3-13_ra_10_25300_misq_2022_17141","article-title":"Neural machine translation by jointly learning to align and translate","author":"Bahdanau","year":"2015"},{"issue":"1","key":"2025082212311716600_b4-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"185","DOI":"10.25300\/MISQ\/2020\/14644","article-title":"Connecting systems, data, and people: A multidisciplinary research roadmap for chronic disease management","volume":"44","author":"Bardhan","year":"2020","journal-title":"Management Information Systems Quarterly"},{"issue":"1","key":"2025082212311716600_b5-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1287\/isre.2014.0553","article-title":"Predictive analytics for readmission of patients with congestive heart failure","volume":"26","author":"Bardhan","year":"2015","journal-title":"Information Systems Research"},{"issue":"1","key":"2025082212311716600_b6-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"201","DOI":"10.25300\/MISQ\/2020\/15101","article-title":"Trajectories of repeated readmissions of chronic disease patients: Risk stratification, profiling, and prediction","volume":"44","author":"Ben-Assuli","year":"2020","journal-title":"MIS Quarterly"},{"issue":"11","key":"2025082212311716600_b7-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"crossref","first-page":"4118","DOI":"10.1210\/jcem.85.11.6953","article-title":"Fracture risk reduction with alendronate in women with osteoporosis: The fracture intervention trial","volume":"85","author":"Black","year":"2000","journal-title":"The Journal of Clinical Endocrinology & Metabolism"},{"issue":"10","key":"2025082212311716600_b8-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"crossref","first-page":"2076","DOI":"10.1016\/j.socscimed.2008.01.034","article-title":"Markets, information asymmetry and health care: Towards new social contracts","volume":"66","author":"Bloom","year":"2008","journal-title":"Social Science & Medicine"},{"key":"2025082212311716600_b9-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"3121","DOI":"10.1109\/ICPR.2010.764","article-title":"The balanced accuracy and its posterior distribution","author":"Brodersen","year":"2010"},{"key":"2025082212311716600_b10-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"Journal of Artificial Intelligence Research"},{"key":"2025082212311716600_b11-13_ra_10_25300_misq_2022_17141","first-page":"371","article-title":"Interpretable deep models for ICU outcome prediction","author":"Che","year":"2016","journal-title":"AMIA Annual Symposium Proceedings"},{"key":"2025082212311716600_b12-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"XGBoost: A scalable tree boosting system","author":"Chen","year":"2016"},{"key":"2025082212311716600_b13-13_ra_10_25300_misq_2022_17141","first-page":"3504","article-title":"RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism","author":"Choi","year":"2016"},{"key":"2025082212311716600_b14-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-319-98131-4_1","article-title":"Considerations for evaluation and generalization in interpretable machine learning","volume-title":"Explainable and interpretable models in computer vision and machine learning","author":"Doshi-Velez","year":"2018"},{"issue":"2","key":"2025082212311716600_b15-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1214\/009053604000000067","article-title":"Least angle regression","volume":"32","author":"Efron","year":"2004","journal-title":"The Annals of Statistics"},{"issue":"L119","key":"2025082212311716600_b16-13_ra_10_25300_misq_2022_17141","first-page":"1","article-title":"Regulation (EU) 2016\/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95\/46\/EC (General Data Protection Regulation)","volume":"59","author":"European Parliament and Council of the European Union","year":"2016","journal-title":"Official Journal of the European Union"},{"issue":"1","key":"2025082212311716600_b18-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"83","DOI":"10.25300\/MISQ\/2021\/14372","article-title":"A Prescriptive analytics method for cost reduction in clinical decision making","volume":"45","author":"Fang","year":"2021","journal-title":"MIS Quarterly"},{"issue":"3","key":"2025082212311716600_b19-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1287\/isre.1110.0382","article-title":"Editorial overview\u2014The role of information systems in healthcare: Current research and future trends","volume":"22","author":"Fichman","year":"2011","journal-title":"Information Systems Research"},{"issue":"5","key":"2025082212311716600_b20-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting approach","volume":"29","author":"Friedman","year":"2001","journal-title":"The Annals of Statistics"},{"issue":"4","key":"2025082212311716600_b21-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"497","DOI":"10.2307\/249487","article-title":"Explanations from intelligent systems: Theoretical foundations and implications for practice","volume":"23","author":"Gregor","year":"1999","journal-title":"MIS Quarterly"},{"key":"2025082212311716600_b22-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2020.103428","article-title":"Evaluating local explanation methods on ground truth","volume":"291","author":"Guidotti","year":"2021","journal-title":"Artificial Intelligence"},{"key":"2025082212311716600_b23-13_ra_10_25300_misq_2022_17141","unstructured":"Gunning, D.\n           (2017, November). Explainable artificial intelligence (XAI). Defense Advanced Research Projects Agency (DARPA), DARPA. https:\/\/www.darpa.mil\/attachments\/XAIProgramUpdate.pdf"},{"issue":"1","key":"2025082212311716600_b24-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"crossref","first-page":"75","DOI":"10.2307\/25148625","article-title":"Design science in information systems research","volume":"28","author":"Hevner","year":"2004","journal-title":"MIS Quarterly"},{"key":"2025082212311716600_b25-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1145\/3292500.3330930","article-title":"Improving the quality of explanations with local embedding perturbations","author":"Jia","year":"2019"},{"issue":"3","key":"2025082212311716600_b26-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1287\/isre.2020.0980","article-title":"Augmenting medical diagnosis decisions? An investigation into physicians\u2019 decision-making process with artificial intelligence","volume":"32","author":"Jussupow","year":"2021","journal-title":"Information Systems Research"},{"issue":"7","key":"2025082212311716600_b27-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1007\/s00198-004-1734-y","article-title":"Alcohol intake as a risk factor for fracture","volume":"16","author":"Kanis","year":"2005","journal-title":"Osteoporosis International"},{"issue":"5","key":"2025082212311716600_b28-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1016\/j.bone.2009.01.373","article-title":"FRAX\u00ae and its applications to clinical practice","volume":"44","author":"Kanis","year":"2009","journal-title":"Bone"},{"key":"2025082212311716600_b29-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.2445102","author":"Kenneally","year":"2012","journal-title":"The Menlo Report: Ethical principles guiding information and communication technology research. US Department of Homeland Security"},{"key":"2025082212311716600_b30-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"113302","DOI":"10.1016\/j.dss.2020.113302","article-title":"Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information","volume":"134","author":"Kim","year":"2020","journal-title":"Decision Support Systems"},{"issue":"2","key":"2025082212311716600_b31-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/dyv316","article-title":"Cohort profile: The Korean Genome and Epidemiology Study (KoGES) consortium","volume":"46","author":"Kim","year":"2017","journal-title":"International Journal of Epidemiology"},{"key":"2025082212311716600_b32-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1145\/2678025.2701399","article-title":"Principles of explanatory debugging to personalize interactive machine learning","author":"Kulesza","year":"2015"},{"key":"2025082212311716600_b33-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1007\/978-3-319-91473-2_9","article-title":"Comparison-based inverse classification for interpretability in machine learning","author":"Laugel","year":"2018"},{"key":"2025082212311716600_b34-13_ra_10_25300_misq_2022_17141","first-page":"47","article-title":"Defining locality for surrogates in post-hoc interpretablity","author":"Laugel","year":"2018"},{"issue":"7553","key":"2025082212311716600_b35-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"9","key":"2025082212311716600_b36-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","DOI":"10.1002\/jbm4.10192","article-title":"Healthcare policy changes in osteoporosis can improve outcomes and reduce costs in the United States","volume":"3","author":"Lewiecki","year":"2019","journal-title":"JBMR Plus"},{"issue":"2","key":"2025082212311716600_b37-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"473","DOI":"10.25300\/MISQ\/2017\/41.2.07","article-title":"Healthcare predictive analytics for risk profiling in chronic care: A Bayesian multitask learning approach","volume":"41","author":"Lin","year":"2017","journal-title":"MIS Quarterly"},{"key":"2025082212311716600_b38-13_ra_10_25300_misq_2022_17141","unstructured":"Lipton, Z. C.\n           (2016). The mythos of model interpretability. In Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning. Available at https:\/\/arxiv.org\/abs\/1606.03490"},{"issue":"3","key":"2025082212311716600_b39-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0246653","article-title":"Association of lower urinary tract symptoms and hip fracture in adults aged \u2265 50 years","volume":"16","author":"Liu","year":"2021","journal-title":"PLoS One"},{"issue":"1","key":"2025082212311716600_b40-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"257","DOI":"10.25300\/MISQ\/2020\/15107","article-title":"Go to YouTube and call me in the morning: Use of social media for chronic conditions","volume":"44","author":"Liu","year":"2020","journal-title":"MIS Quarterly"},{"key":"2025082212311716600_b41-13_ra_10_25300_misq_2022_17141","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"2","key":"2025082212311716600_b42-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1287\/isre.2014.0513","article-title":"A machine learning approach to improving dynamic decision making","volume":"25","author":"Meyer","year":"2014","journal-title":"Information Systems Research"},{"key":"2025082212311716600_b43-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","article-title":"Explanation in artificial intelligence: Insights from the social sciences","volume":"267","author":"Miller","year":"2019","journal-title":"Artificial Intelligence"},{"key":"2025082212311716600_b44-13_ra_10_25300_misq_2022_17141","volume-title":"The need for biases in learning generalizations","author":"Mitchell","year":"1980"},{"key":"2025082212311716600_b45-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"22071","DOI":"10.1073\/pnas.1900654116","article-title":"Interpretable machine learning: Definitions, methods, and applications","author":"Murdoch","year":"2019"},{"key":"2025082212311716600_b46-13_ra_10_25300_misq_2022_17141","unstructured":"National Clinical Guideline Centre\n          . (2012). Osteoporosis: Fragility fracture risk. National Institute for Health Care and Excellence. https:\/\/www.nice.org.uk\/Guidance\/CG146"},{"issue":"3","key":"2025082212311716600_b47-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1080\/07421222.2015.1094961","article-title":"The last research mile: Achieving both rigor and relevance in information systems research","volume":"32","author":"Nunamaker","year":"2015","journal-title":"Journal of Management Information Systems"},{"issue":"3","key":"2025082212311716600_b48-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1080\/07421222.1990.11517898","article-title":"Systems development in information systems research","volume":"7","author":"Nunamaker","year":"1990","journal-title":"Journal of Management Information Systems"},{"key":"2025082212311716600_b49-13_ra_10_25300_misq_2022_17141","unstructured":"Poyiadzi, R., Renard, X., Laugel, T., Santos-Rodriguez, R., & Detyniecki, M. (2021). Understanding surrogate explanations: The interplay between complexity, fidelity and coverage. Available at https:\/\/arxiv.org\/abs\/2107.04309"},{"issue":"7","key":"2025082212311716600_b50-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"2430","DOI":"10.3390\/ijerph17072430","article-title":"A new application of social impact in social media for overcoming fake news in health","volume":"17","author":"Pulido","year":"2020","journal-title":"International Journal of Environmental Research and Public Health"},{"issue":"2","key":"2025082212311716600_b51-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"crossref","first-page":"iii","DOI":"10.25300\/MISQ\/2016\/40.2.E0","article-title":"Synergies Between big data and theory","volume":"40","author":"Rai","year":"2016","journal-title":"MIS Quarterly"},{"issue":"1","key":"2025082212311716600_b52-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s11747-019-00710-5","article-title":"Explainable AI: From black box to glass box","volume":"48","author":"Rai","year":"2020","journal-title":"Journal of the Academy of Marketing Science"},{"issue":"1","key":"2025082212311716600_b53-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"crossref","first-page":"479","DOI":"10.25300\/MISQ\/2021\/15434.1.5","article-title":"Focusing on programmatic high impact information systems research, not theory, to address grand challenges","volume":"45","author":"Ram","year":"2021","journal-title":"MIS Quarterly"},{"issue":"2","key":"2025082212311716600_b54-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.bone.2005.11.024","article-title":"Osteoporosis: A still increasing prevalence","volume":"38","author":"Reginster","year":"2006","journal-title":"Bone"},{"key":"2025082212311716600_b55-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"crossref","DOI":"10.1145\/2939672.2939778","article-title":"Why should I trust you?\u201d: Explaining the predictions of any classifier","author":"Ribeiro","year":"2016"},{"issue":"5","key":"2025082212311716600_b56-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nature Machine Intelligence"},{"key":"2025082212311716600_b57-13_ra_10_25300_misq_2022_17141","volume-title":"The sciences of the artificial","author":"Simon","year":"1996"},{"key":"2025082212311716600_b58-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"crossref","unstructured":"Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020). Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods. In Proceedings of the AAAI\/ACM Conference on AI, Ethics, and Society, 180-186. Available at https:\/\/arxiv.org\/abs\/1911.02508","DOI":"10.1145\/3375627.3375830"},{"key":"2025082212311716600_b59-13_ra_10_25300_misq_2022_17141","article-title":"Practical Bayesian optimization of machine learning algorithms","author":"Snoek","year":"2012"},{"issue":"1","key":"2025082212311716600_b60-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"285","DOI":"10.25300\/MISQ\/2020\/15092","article-title":"A data analytics framework for smart asthma management based on remote health information systems with bluetooth-enabled personal inhalers","volume":"44","author":"Son","year":"2020","journal-title":"MIS Quarterly"},{"issue":"2","key":"2025082212311716600_b61-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1177\/1098214005283748","article-title":"A general inductive approach for analyzing qualitative evaluation data","volume":"27","author":"Thomas","year":"2006","journal-title":"American Journal of Evaluation"},{"issue":"1","key":"2025082212311716600_b62-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"227","DOI":"10.25300\/MISQ\/2020\/15085","article-title":"Chronic disease management: How IT and analytics create healthcare value through the temporal displacement of care","volume":"44","author":"Thompson","year":"2020","journal-title":"MIS Quarterly"},{"issue":"3","key":"2025082212311716600_b63-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"1557","DOI":"10.25300\/MISQ\/2021\/16559","article-title":"When the machine meets the expert: An ethnography of developing AI for hiring","volume":"45","author":"van den Broek","year":"2021","journal-title":"MIS Quarterly"},{"issue":"6","key":"2025082212311716600_b64-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1007\/s12603-010-0110-1","article-title":"Percent body fat, fractures and risk of osteoporosis in women","volume":"14","author":"Wyshak","year":"2010","journal-title":"The Journal of Nutrition, Health & Aging"},{"key":"2025082212311716600_b65-13_ra_10_25300_misq_2022_17141","unstructured":"Yang, Y., Morillo, I. G., & Hospedales, T. M. (2018, June 18). Deep neural decision trees. In Proceedings of the 2018 ICML Workshop on Human Interpretability in Machine Learning. Available at https:\/\/arxiv.org\/abs\/1806.06988"},{"issue":"1","key":"2025082212311716600_b66-13_ra_10_25300_misq_2022_17141","doi-asserted-by":"publisher","first-page":"305","DOI":"10.25300\/MISQ\/2020\/15106","article-title":"A comprehensive analysis of triggers and risk factors for asthma based on machine learning and large heterogeneous data sources","volume":"44","author":"Zhang","year":"2020","journal-title":"MIS Quarterly"}],"container-title":["MIS Quarterly"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/misq.umn.edu\/misq\/article-pdf\/47\/3\/1303\/9031\/13_ra_10_25300_misq_2022_17141.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/misq.umn.edu\/misq\/article-pdf\/47\/3\/1303\/9031\/13_ra_10_25300_misq_2022_17141.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T16:31:27Z","timestamp":1755880287000},"score":1,"resource":{"primary":{"URL":"https:\/\/misq.umn.edu\/misq\/article\/47\/3\/1303\/2241\/ROLEX-A-Novel-Method-for-Interpretable-Machine"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,1]]},"references-count":65,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,9,1]]},"published-print":{"date-parts":[[2023,9,1]]}},"URL":"https:\/\/doi.org\/10.25300\/misq\/2022\/17141","relation":{},"ISSN":["0276-7783","2162-9730"],"issn-type":[{"value":"0276-7783","type":"print"},{"value":"2162-9730","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,1]]}}}