Interpretable machine learning
This work synthesizes foundational work on IML methods and evaluation into an actionable taxonomy that serves as a tool to conceptualize the gap between researchers and consumers, illustrated by the lack of connections between its methods and use cases components.
Abstract
research-article Open Access Share on Interpretable machine learning: moving from mythos to diagnostics Authors: Valerie Chen Carnegie Mellon University, Pittsburgh, PA Carnegie Mellon University, Pittsburgh, PAView Profile , Jeffrey Li University of Washington, Seattle, WA University of Washington, Seattle, WAView Profile , Joon Sik Kim Carnegie Mellon University Carnegie Mellon UniversityView Profile , Gregory Plumb Carnegie Mellon University, Pittsburgh, PA Carnegie Mellon University, Pittsburgh, PAView Profile , Ameet Talwalkar Carnegie Mellon University, Pittsburgh, PA Carnegie Mellon University, Pittsburgh, PAView Profile Authors Info & Claims Communications of the ACMVolume 65Issue 8August 2022 pp 43–50https://doi.org/10.1145/3546036Published:21 July 2022Publication History 0citation8,320DownloadsMetricsTotal Citations0Total Downloads8,320Last 12 Months8,320Last 6 weeks544 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF