Trust in artificial intelligence within production management - an exploration of antecedents

被引：22

作者：

Sassmannshausen, Till ^{[1
]}

Burggraef, Peter ^{[1
]}

Wagner, Johannes ^{[1
]}

Hassenzahl, Marc ^{[2
]}

Heupel, Thomas ^{[3
]}

Steinberg, Fabian ^{[1
]}

机构：

[1] Univ Siegen, Int Prod Engn & Management, Siegen, Germany

[2] Univ Siegen, Ubiquitous Design, Siegen, Germany

[3] FOM Univ Appl Sci, Siegen, Germany

来源：

ERGONOMICS | 2021年 / 64卷 / 10期

关键词：

Production management; cyber production management; trust; artificial intelligence; machine learning; PLS-SEM; AUTOMATION; MODEL; METAANALYSIS; INFORMATION; PERFORMANCE; TECHNOLOGY; SYSTEM;

D O I：

10.1080/00140139.2021.1909755

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Industry 4.0, big data, predictive analytics, and robotics are leading to a paradigm shift on the shop floor of industrial production. However, complex, cognitive tasks are also subject of change, due to the development of artificial intelligence (AI). Smart assistants are finding their way into the world of knowledge work and require cooperation with humans. Here, trust is an essential factor that determines the success of human-AI cooperation. Within this article, an analysis within production management identifies possible antecedent variables on trust in AI and evaluates these due to interaction scenarios with AI. The results of this research are five antecedents for human trust in AI within production management. From these results, preliminary design guidelines are derived for a socially sustainable human-AI interaction in future production management systems. Practitioner summary: In the future, artificial intelligence will assist cognitive tasks in production management. In order to make good decisions, humans trust in AI has to be well calibrated. For trustful human-AI interactions, it is beneficial that humans subjectively perceive AI as capable and comprehensible and that they themselves are digitally competent.

引用

页码：1333 / 1350

页数：18

共 98 条

[11]

Burggraf P., 2020, Expert Systems with Applications: X, V5, P1, DOI [DOI 10.1016/J.ESWAX.2020.100025, 10.1016/j.eswax.2020.100025]

[12] XGBoost: A Scalable Tree Boosting System [J].

Chen, Tianqi ;

Guestrin, Carlos .

KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, :785-794

[13] Making Reliability Reliable: A Systematic Approach to Reliability Coefficients [J].

Cho, Eunseong .

ORGANIZATIONAL RESEARCH METHODS, 2016, 19 (04) :651-682

[14]

Cohen J., 1988, Statistical Power Analysis For The Behavioral Sciences, DOI [10.4324/9780203771587, DOI 10.4324/9780203771587]

[15] WHAT IS COEFFICIENT ALPHA - AN EXAMINATION OF THEORY AND APPLICATIONS [J].

CORTINA, JM .

JOURNAL OF APPLIED PSYCHOLOGY, 1993, 78 (01) :98-104

[16]

Cutler A., 2019, EVERYDAY ETHICS ARTI

[17] From "automation' to "autonomy': the importance of trust repair in human-machine interaction [J].

de Visser, Ewart J. ;

Pak, Richard ;

Shaw, Tyler H. .

ERGONOMICS, 2018, 61 (10) :1409-1427

[18] Hybrid Intelligence [J].

Dellermann, Dominik ;

Ebel, Philipp ;

Soellner, Matthias ;

Leimeister, Jan Marco .

BUSINESS & INFORMATION SYSTEMS ENGINEERING, 2019, 61 (05) :637-643

[19]

Detweiler C, 2009, LECT NOTES COMPUT SC, V5612, P605, DOI 10.1007/978-3-642-02580-8_66

[20] CONSISTENT PARTIAL LEAST SQUARES PATH MODELING [J].

Dijkstra, Theo K. ;

Henseler, Jorg .

MIS QUARTERLY, 2015, 39 (02) :297-+

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