{"type":"ScholarlyArticle","title":"Inspection of Blackbox Models for Evaluating Vulnerability in Maternal, Newborn, and Child Health","authors":[{"name":"William Ogallo","slug":"william-ogallo","given":"William","family":"Ogallo","affiliation":"IBM Research -Africa , Nairobi , Kenya"},{"name":"Skyler Speakman","slug":"skyler-speakman","given":"Skyler","family":"Speakman","affiliation":"IBM Research -Africa , Nairobi , Kenya"},{"name":"Victor Akinwande","slug":"victor-akinwande","given":"Victor","family":"Akinwande","affiliation":"IBM Research -Africa , Nairobi , Kenya"},{"name":"Kush R Varshney","slug":"kush-r-varshney","given":"Kush R.","family":"Varshney","affiliation":"IBM Research -Africa , Nairobi , Kenya; IBM Research -T. J. Watson Research Center , Yorktown Heights , NY , USA"},{"name":"Aisha Walcott-Bryant","slug":"aisha-walcott-bryant","given":"Aisha","family":"Walcott-Bryant","affiliation":"IBM Research -Africa , Nairobi , Kenya"},{"name":"Charity Wayua","slug":"charity-wayua","given":"Charity","family":"Wayua","affiliation":"IBM Research -Africa , Nairobi , Kenya"},{"name":"Komminist Weldemariam","slug":"komminist-weldemariam","given":"Komminist","family":"Weldemariam","affiliation":"IBM Research -Africa , Nairobi , Kenya"}],"abstract":"Improving maternal, newborn, and child health (MNCH) outcomes is a critical target for global sustainable development. Our research is centered on building predictive models, evaluating their interpretability, and generating actionable insights about the markers (features) and triggers (events) associated with vulnerability in MNCH.  In this work, we demonstrate how a tool for inspecting \"black box\" machine learning models can be used to generate actionable insights from models trained on demographic health survey data to predict neonatal mortality.","doi":"10.24963/ijcai.2020/770","url":"https://ijcai.org/proceedings/2020/770","pdf":"https://ijcai.org/proceedings/2020/0770.pdf","markdown":"https://ijcai.org/proceedings/2020/770.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/770","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Demos","track":"Demos","number":770,"pages":{"first":5282,"last":5284},"keywords":["Machine Learning: general"],"videos":[],"references":[{"position":1,"raw":"[Friedman, 2001] Jerome H Friedman. Greedy function ap- proximation: a gradient boosting machine. Annals of Statistics, pages 1189-1232, 2001.","title":"Greedy function approximation: a gradient boosting machine","authors":"Jerome H Friedman","year":"2001","venue":"Annals of Statistics","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Krause et al., 2016] Josua Krause, Adam Perer, and Ken- ney Ng. Interacting with predictions: Visual inspection of black-box machine learning models. In Proceedings of the 2016 CHI Conference on Human Factors in Comput- ing Systems, pages 5686-5697, 2016.","title":"Interacting with predictions: Visual inspection of black-box machine learning models","authors":"Josua Krause, Adam Perer, Kenney Ng","year":"2016","venue":"Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[McFowland et al., 2013] Edward McFowland, Skyler Speakman, and Daniel B Neill. Fast generalized subset scan for anomalous pattern detection. The Journal of Machine Learning Research, 14(1):1533-1561, 2013.","title":"Fast generalized subset scan for anomalous pattern detection","authors":"Edward Mcfowland, Skyler Speakman, Daniel B Neill","year":"2013","venue":"The Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}