Research shows that there are a variety of performance advantages to increasing the degrees of autonomy that human-supporting autonomous systems possess, including organizational productivity and the reduction of human workload. Yet, not all the consequences of increasing autonomy are positive. One such negative consequence is the risk of human operators becoming more incapable of responding to system failures as autonomy levels increase, a phenomenon known as the Lumberjack Effect. This paper proposes a conceptual model for using adaptive autonomy as a means to avoid the risks of this phenomenon while retaining the advantages of higher autonomy levels in earlier stages of an organization’s work cycle. We apply our model to two research-based scenarios and discuss future research necessary to validate the model. This model provides the Human Factors community with a possible solution to the debate over the risks of designing human-supporting autonomous systems with higher degrees of autonomy.
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References
1.
AmatoA.JungJ. J.VenticinqueS. (2017). Extending the internet of energy by a social networking of human users and autonomous agents. Multimedia Tools and Applications, 76(24), 26057–26076.
2.
de GreefT.ArciszewskiH.. (2008). Combining adaptive automation and adaptive teams in a naval command centre. In Proceedings of the 15th european conference on cognitive ergonomics: the ergonomics of cool interaction (pp. 1–4).
3.
De LuciaM. J.NewcombA.KottA.. (2019). Features and operation of an autonomous agent for cyber defense. arXiv preprint arXiv:1905.05253.
4.
De VisserE. J.PakR.ShawT. H.. (2018). From ‘automation’to ‘autonomy’: the importance of trust repair in human–machine interaction. Ergonomics, 61(10), 1409–1427.
5.
EndsleyM. R.KirisE. O. (1995). The out-of-the-loop performance problem and level of control in automation. Human factors, 37(2), 381–394.
6.
GaneshM. I. (2020). The ironies of autonomy. Humanities and Social Sciences Communications, 7(1), 1–10.
7.
HouM.HoG.DunwoodyD. (2021). Impacts: a trust model for human-autonomy teaming. Human-Intelligent Systems Integration, 1–19.
8.
JamiesonG. A.SkraaningG. (2020)a. The absence of degree of automation trade-offs in complex work settings. Human factors, 62(4), 516–529.
9.
JamiesonG. A.SkraaningG. (2020)b. The harder they fall? a response to wickens et al.(2019) regarding the generalizability of lumberjack predictions to complex work settings. Human factors, 62(4), 535–539.
10.
JooM.SeoJ.OhJ.ParkM.LeeK. (2018). Situational awareness framework for cyber crime prevention model in cyber physical system. In 2018 tenth international conference on ubiquitous and future networks (icufn) (pp. 837–842).
11.
KaberD. B.OnalE.EndsleyM. R. (2000). Design of automation for telerobots and the effect on performance, operator situation awareness, and subjective workload. Human factors and ergonomics in manufacturing & service industries, 10(4), 409–430.
12.
KistanT.GardiA.SabatiniR. (2018). Machine learning and cognitive ergonomics in air traffic management: Recent developments and considerations for certification. Aerospace, 5(4), 103.
13.
MartinC. E.BarberK. S. (1996). Multiple, simultaneous autonomy levels for agent-based systems. In Proceedings of the fourth international conference on control, automation, robotics, and vision (pp. 1318–1322).
14.
McGeeE. T.McGregorJ. D. (2016). Using dynamic adaptive systems in safety-critical domains. In Proceedings of the 11th international symposium on software engineering for adaptive and self-managing systems (pp. 115–121).
15.
OnnaschL.WickensC. D.LiH.ManzeyD. (2014). Human performance consequences of stages and levels of automation: An integrated meta-analysis. Human factors, 56(3), 476–488.
16.
O’NeillT.McNeeseN.BarronA.SchelbleB. (2020). Human– autonomy teaming: A review and analysis of the empirical literature. Human Factors, 0018720820960865.
17.
ParasuramanR.RileyV. (1997). Humans and automation: Use, misuse, disuse, abuse. Human factors, 39(2), 230–253.
18.
ParasuramanR.SheridanT.WickensC. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics -Part A: Systems and Humans, 30(3), 286-297. doi: 10.1109/3468.844354
19.
SebokA.WickensC. D. (2017). Implementing lumberjacks and black swans into model-based tools to support human–automation interaction. Human factors, 59(2), 189–203.
20.
SethumadhavanA. (2009). Effects of automation types on air traffic controller situation awareness and performance. In Proceedings of the human factors and ergonomics society annual meeting (Vol. 53, pp. 1–5).
21.
Suzanne BarberK.GoelA.MartinC. E. (2000). Dynamic adaptive autonomy in multi-agent systems. Journal of Experimental & Theoretical Artificial Intelligence, 12(2), 129–147.
22.
TenhundfeldN. L.de VisserE. J.HaringK. S.RiesA. J.FinomoreV. S.TossellC. C. (2019). Calibrating trust in automation through familiarity with the autoparking feature of a tesla model x. Journal of cognitive engineering and decision making, 13(4), 279–294.
23.
WickensC. D.LiH.SantamariaA.SebokA.SarterN. B. (2010). Stages and levels of automation: An integrated meta-analysis. In Proceedings of the human factors and ergonomics society annual meeting (Vol. 54, pp. 389–393).
24.
WickensC. D.OnnaschL.SebokA.ManzeyD. (2020). Absence of doa effect but no proper test of the lumberjack effect: A reply to jamieson and skraaning (2019). Human factors, 62(4), 530–534.
25.
XuW. (2020). From automation to autonomy and autonomous vehicles: Challenges and opportunities for human-computer interaction. Interactions, 28(1), 48–53.
