Cybertrust : From Explainable to

Igor Linkov; Stephanie Galaitsi; Benjamin D Trump; U S Army Corps; Jeffrey M Keisler

Cybertrust : From Explainable to

Igor Linkov
, Stephanie Galaitsi
, Benjamin D Trump
, U S Army Corps
, Jeffrey M Keisler

Management Science & Info Sys

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

Abstract

We argue that artificial intelligence (AI) systems should be designed with features that build trust by bringing decision-analytic perspectives into AI. Actionable and interpretable AI will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.

Original language	American English
Pages (from-to)	91-96
Number of pages	6
Journal	IEEE Access
Volume	53
Issue number	September
State	Published - 2020

Cite this

@article{e41143198f9a41838a2399ff227b9e2d,

title = "Cybertrust : From Explainable to",

abstract = "We argue that artificial intelligence (AI) systems should be designed with features that build trust by bringing decision-analytic perspectives into AI. Actionable and interpretable AI will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.",

author = "Igor Linkov and Stephanie Galaitsi and Trump, \{Benjamin D\} and Corps, \{U S Army\} and Keisler, \{Jeffrey M\}",

year = "2020",

language = "American English",

volume = "53",

pages = "91--96",

journal = "IEEE Access",

issn = "2169-3536",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "September",

}

TY - JOUR

T1 - Cybertrust : From Explainable to

AU - Linkov, Igor

AU - Galaitsi, Stephanie

AU - Trump, Benjamin D

AU - Corps, U S Army

AU - Keisler, Jeffrey M

PY - 2020

Y1 - 2020

N2 - We argue that artificial intelligence (AI) systems should be designed with features that build trust by bringing decision-analytic perspectives into AI. Actionable and interpretable AI will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.

AB - We argue that artificial intelligence (AI) systems should be designed with features that build trust by bringing decision-analytic perspectives into AI. Actionable and interpretable AI will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.

UR - https://ieeexplore-ieee-org.myezproxy.vub.ac.be/stamp/stamp.jsp?tp=&arnumber=9187463

UR - https://www.mendeley.com/catalogue/49803acd-03ad-3e3c-a8fa-4c29e39dae56/

M3 - Article

SN - 2169-3536

VL - 53

SP - 91

EP - 96

JO - IEEE Access

JF - IEEE Access

IS - September

ER -

Cybertrust : From Explainable to

Abstract

Other files and links

Fingerprint

Cite this