%0 Conference Proceedings
%T Responsible Machine Learning Pilot Test Projects: A Medical Coding Case Study
%+ University of South Florida [Tampa] (USF)
%+ Bentley University
%A Champagnie, Samantha
%A Gogan, Janis, L.
%Z Part 1: Adopting AI for Digital Transformation and Public Good
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 20th Conference on e-Business, e-Services and e-Society (I3E)
%C Galway, Ireland
%Y Denis Dennehy
%Y Anastasia Griva
%Y Nancy Pouloudi
%Y Yogesh K. Dwivedi
%Y Ilias Pappas
%Y Matti Mäntymäki
%I Springer International Publishing
%3 Responsible AI and Analytics for an Ethical and Inclusive Digitized Society
%V LNCS-12896
%P 94-106
%8 2021-09-01
%D 2021
%R 10.1007/978-3-030-85447-8_9
%K AI
%K Machine learning
%K Ethics
%K Governance
%K NLP
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X Prior studies reported on many machine learning (ML) projects that under-performed. What steps can leaders take during ML pilot projects to identify and mitigate project risks and systems risks, before implementing new ML systems at scale? We report on an exploratory case study of a U.S.-based healthcare provider organization’s ML pilot project, undertaken when a software vendor proposed an automated solution that would combine natural language processing (NLP) and ML, to improve medical claims coding quality. We reveal tactics the client took during the pilot project, to spot and limit risks that could ultimately harm the firm, its healthcare providers, and its patients. We conclude with suggestions for further research on responsible ML.
%G English
%Z TC 6
%Z WG 6.11
%2 https://inria.hal.science/hal-03648120/document
%2 https://inria.hal.science/hal-03648120/file/512902_1_En_9_Chapter.pdf
%L hal-03648120
%U https://inria.hal.science/hal-03648120
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-WG6-11
%~ IFIP-I3E
%~ IFIP-LNCS-12896