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Air Traffic Leadership Perceptions on the Use of Machine Learning for Air Traffic Safety.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Air Traffic Leadership Perceptions on the Use of Machine Learning for Air Traffic Safety./
Author:	Boyer, Corey J.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	115 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
Subject:	Information technology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28547839
ISBN:	9798534649727

Air Traffic Leadership Perceptions on the Use of Machine Learning for Air Traffic Safety.
Boyer, Corey J.

Air Traffic Leadership Perceptions on the Use of Machine Learning for Air Traffic Safety. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 115 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--University of the Cumberlands, 2020.

This item must not be sold to any third party vendors.

Air traffic organizations today collect massive amounts of data to learn from past events in order to make flying safer. However, analysts in air traffic organizations only have the ability to analyze a fraction of this data using traditional analytical methods due to limited resources. Given this limitation, air traffic organizations have turned to machine learning to make better use of large datasets. While machine learning tools and analysis have been proven to be effective across many industry domains - including air traffic - air traffic decision makers may be reluctant to use them for a multitude of reasons. This study used a causal-comparative research design to examine the perceptions of 118 air traffic leaders on the use of machine learning for air traffic safety. The results of the study revealed an overall lack of trust amongst air traffic leaders on how the information obtained from machine learning will be used and who will have access. This lack of trust could result in diminished use of machine learning for air traffic safety which could make it less safe to fly.

ISBN: 9798534649727Subjects--Topical Terms:

532993
Information technology.
Subjects--Index Terms:

Air traffic

Air Traffic Leadership Perceptions on the Use of Machine Learning for Air Traffic Safety.
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500 $a Advisor: Nichols, Jessica.
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506 $a This item must not be sold to any third party vendors.
520 $a Air traffic organizations today collect massive amounts of data to learn from past events in order to make flying safer. However, analysts in air traffic organizations only have the ability to analyze a fraction of this data using traditional analytical methods due to limited resources. Given this limitation, air traffic organizations have turned to machine learning to make better use of large datasets. While machine learning tools and analysis have been proven to be effective across many industry domains - including air traffic - air traffic decision makers may be reluctant to use them for a multitude of reasons. This study used a causal-comparative research design to examine the perceptions of 118 air traffic leaders on the use of machine learning for air traffic safety. The results of the study revealed an overall lack of trust amongst air traffic leaders on how the information obtained from machine learning will be used and who will have access. This lack of trust could result in diminished use of machine learning for air traffic safety which could make it less safe to fly.
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653 $a Air traffic
653 $a Machine learning
653 $a Operational metrics
653 $a Analytical capabilities
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