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Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

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Front. Eng    2022, Vol. 9 Issue (1) : 89-103    https://doi.org/10.1007/s42524-021-0182-0
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Challenges of human–machine collaboration in risky decision-making
Wei XIONG, Hongmiao FAN, Liang MA(), Chen WANG()
Laboratory of Enhanced Human–Machine Collaborative Decision-Making, Department of Industrial Engineering, Tsinghua University, Beijing 100084, China
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Abstract

The purpose of this paper is to delineate the research challenges of human–machine collaboration in risky decision-making. Technological advances in machine intelligence have enabled a growing number of applications in human–machine collaborative decision-making. Therefore, it is desirable to achieve superior performance by fully leveraging human and machine capabilities. In risky decision-making, a human decision-maker is vulnerable to cognitive biases when judging the possible outcomes of a risky event, whereas a machine decision-maker cannot handle new and dynamic contexts with incomplete information well. We first summarize features of risky decision-making and possible biases of human decision-makers therein. Then, we argue the necessity and urgency of advancing human–machine collaboration in risky decision-making. Afterward, we review the literature on human–machine collaboration in a general decision context, from the perspectives of human–machine organization, relationship, and collaboration. Lastly, we propose challenges of enhancing human–machine communication and teamwork in risky decision-making, followed by future research avenues.

Keywords human–machine collaboration      risky decision-making      human–machine team and interaction      task allocation      human–machine relationship     
Corresponding Author(s): Liang MA,Chen WANG   
Just Accepted Date: 17 December 2021   Online First Date: 19 January 2022    Issue Date: 14 February 2022
 Cite this article:   
Wei XIONG,Hongmiao FAN,Liang MA, et al. Challenges of human–machine collaboration in risky decision-making[J]. Front. Eng, 2022, 9(1): 89-103.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-021-0182-0
https://academic.hep.com.cn/fem/EN/Y2022/V9/I1/89
Fig.1  Human decision-maker’s limitations and machine’s potential to enhance risky decision-making.
Fig.2  Research opportunities on human–machine collaboration in risky decision-making.
Fig.3  Literature perspectives on human–machine collaboration.
Topic Main focus Ref.
Task/Function allocation Capability Fitts (1951)
Capability, task (definition, process) Chignell and Hancock (1986); Parasuraman et al. (2000)
Capability, task, human–machine team Daugherty and Wilson (2018); Kunnathuvalappil Hariharan (2018); Karstens et al. (2018); Patel et al. (2019); Seeber et al. (2020); Tschandl et al. (2020)
Task requirements, interdependence requirements Roth et al. (2019)
Openness of the problem, known risk level Saenz et al. (2020)
Dynamic allocation Dynamic characteristics (status, trust, workload, etc.) Chignell and Hancock (1986); Lee and Moray (1992); Lee and See (2004); Wickens et al. (2013); Hancock et al. (2020); Dubois and Le Ny (2020)
Tab.1  Summary of studies on task allocation within human–machine teams
Topic Main focus Ref.
Human–machine relationship User and tool, pilot and co-pilot Hoc (2000); Urlings and Jain (2002)
Human-centered AI Gunning (2016); de Visser et al. (2018); Li and Etchemendy (2018); Xu (2019)
Machine as teammates Phillips et al. (2011); Lyons et al. (2018); Wynne and Lyons (2018); Seeber et al. (2020)
Tab.2  Summary of studies on human–machine relationship
Topic Main focus Ref.
Physical interface Information display, the control of a machine/system Fitts and Seeger (1953); Bradley (1954); Ortiz and Park (2011)
Transparency, trust in automation Seong and Bisantz (2008); Wickens et al. (2013); Hoff and Bashir (2015); Skraaning and Jamieson (2019)
Transparent interface, mental model, effective human–machine team Speier (2006); Kreye et al. (2012); Schaefer et al. (2017); Seeber et al. (2019); Ferrari (2019); Hancock et al. (2020); Gutzwiller and Reeder (2021)
Tab.3  Summary of studies on the physical interface of human–machine interaction
Topic Main focus Ref.
Attitudes toward machine (attitudes) Influencing factors, models and theories of acceptance Fishbein and Ajzen (1975); Davis et al. (1989); Cramer et al. (2008); Kuo et al. (2009); Venkatesh et al. (2012); Gursoy et al. (2019); Du et al. (2019); Yalçın and DiPaola (2020)
Influencing factors, models and frameworks of trust Sheridan and Hennessy (1984); Lee and See (2004); McGuirl and Sarter (2006); Madhavan and Wiegmann (2007); Hancock et al. (2011); Hoff and Bashir (2015); Salem et al. (2015); Schaefer et al. (2016; 2017); Wang et al. (2016); Akash et al. (2017); Kraus et al. (2020)
Mental representation of machine (understanding) Mental model, shared mental model Cannon-Bowers et al. (1993); Johnson-Laird (1996); Gentner (2001); Vosgerau (2006); Kulesza et al. (2009); Ososky et al. (2012); Laid et al. (2020); Shin (2020)
Situation awareness, shared situation awareness, situation awareness-based agent transparency models Endsley (1988; 1995); Ososky et al. (2012); Selkowitz et al. (2016); Stowers et al. (2016); Chen et al. (2018); Bhardwaj et al. (2020)
Tab.4  Summary of studies on the mental interface of human–machine interaction
Fig.4  Summary of challenges of human–machine collaboration in risky decision-making.
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