Model Interpretation and Explainability towards Creating Transparency in Prediction Models.

Daniel R. Dolk; Donald J. Kridel; Jacob Dineen; David G. Castillo

doi:10.24251/HICSS.2020.120

Model Interpretation and Explainability towards Creating Transparency in Prediction Models.

Daniel R. Dolk, Donald J. Kridel, Jacob Dineen, David G. Castillo

University of Missouri-St. Louis

Research output: Contribution to journal › Article › peer-review

Original language	American English
Journal	Hawaii International Conference on System Sciences
DOIs	https://doi.org/10.24251/HICSS.2020.120
State	Published - Jan 1 2020

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Management Sciences and Quantitative Methods
Computer Sciences

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10.24251/HICSS.2020.120License: Unspecified

Cite this

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title = "Model Interpretation and Explainability towards Creating Transparency in Prediction Models.",

author = "Dolk, {Daniel R.} and Kridel, {Donald J.} and Jacob Dineen and Castillo, {David G.}",

note = "In view of the current Corona Virus epidemic, Schloss Dagstuhl has moved its 2020 proposal submission period to July 1 to July 15, 2020, and there will not be another proposal round in November 2020. Please consider submitting your proposal for future Dagstuhl Seminars & Workshops. For more information, see our Call for Proposals.",

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doi = "10.24251/HICSS.2020.120",

language = "American English",

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T1 - Model Interpretation and Explainability towards Creating Transparency in Prediction Models.

AU - Dolk, Daniel R.

AU - Kridel, Donald J.

AU - Dineen, Jacob

AU - Castillo, David G.

N1 - In view of the current Corona Virus epidemic, Schloss Dagstuhl has moved its 2020 proposal submission period to July 1 to July 15, 2020, and there will not be another proposal round in November 2020. Please consider submitting your proposal for future Dagstuhl Seminars & Workshops. For more information, see our Call for Proposals.

PY - 2020/1/1

Y1 - 2020/1/1

UR - https://dblp.uni-trier.de/db/conf/hicss/hicss2020.html#DolkKDC20

U2 - 10.24251/HICSS.2020.120

DO - 10.24251/HICSS.2020.120

M3 - Article

JO - Hawaii International Conference on System Sciences

JF - Hawaii International Conference on System Sciences

ER -

Model Interpretation and Explainability towards Creating Transparency in Prediction Models.

Disciplines

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