Human Performance Consequences of Stages and Levels of Automation: An Integrated Meta-Analysis

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Objective: We investigated how automation-induced human performance consequences depended on the degree of automation (DOA). Background: Function allocation between human and automation can be represented in terms of the stages and levels taxonomy proposed by Parasuraman, Sheridan, and Wickens. Higher DOAs are achieved both by later stages and higher levels within stages. Method: A meta-analysis based on data of 18 experiments examines the mediating effects of DOA on routine system performance, performance when the automation fails, workload, and situation awareness (SA). The effects of DOA on these measures are summarized by level of statistical significance. Results: We found (a) a clear automation benefit for routine system performance with increasing DOA, (b) a similar but weaker pattern for workload when automation functioned properly, and (c) a negative impact of higher DOA on failure system performance and SA. Most interesting was the finding that negative consequences of automation seem to be most likely when DOA moved across a critical boundary, which was identified between automation supporting information analysis and automation supporting action selection. Conclusion: Results support the proposed cost–benefit trade-off with regard to DOA. It seems that routine performance and workload on one hand, and the potential loss of SA and manual skills on the other hand, directly trade off and that appropriate function allocation can serve only one of the two aspects. Application: Findings contribute to the body of research on adequate function allocation by providing an overall picture through quantitatively combining data from a variety of studies across varying domains

Automation in human-machine networks: how increasing machine agency affects human agency

© 2018, Springer International Publishing AG. Efficient human-machine networks require productive interaction between human and machine actors. In this study, we address how a strengthening of machine agency, for example through increasing levels of automation, affect the human actors of the networks. Findings from case studies within air traffic management, emergency management, and crowd evacuation are presented, shedding light on how automation may strengthen the agency of human actors in the network through responsibility sharing and task allocation, and serve as a needed prerequisite of innovation and change

Clinical mastitis in ewes; bacteriology, epidemiology and clinical features

<p>Abstract</p> <p>Background</p> <p>Clinical mastitis is an important disease in sheep. The objective of this work was to identify causal bacteria and study certain epidemiological and clinical features of clinical mastitis in ewes kept for meat and wool production.</p> <p>Methods</p> <p>The study included 509 ewes with clinical mastitis from 353 flocks located in 14 of the 19 counties in Norway. Clinical examination and collection of udder secretions were carried out by veterinarians. Pulsed-field gel electrophoresis (PFGE) was performed on 92 <it>Staphylococcus aureus </it>isolates from 64 ewes.</p> <p>Results and conclusion</p> <p><it>S. aureus </it>was recovered from 65.3% of 547 clinically affected mammary glands, coagulase-negative staphylococci from 2.9%, enterobacteria, mainly <it>Escherichia coli</it>, from 7.3%, <it>Streptococcus </it>spp. from 4.6%, <it>Mannheimia haemolytica </it>from 1.8% and various other bacteria from 4.9%, while no bacteria were cultured from 13.2% of the samples. Forty percent of the ewes with unilateral clinical <it>S. aureus </it>mastitis also had a subclinical <it>S. aureus </it>infection in the other mammary gland. Twenty-four of 28 (86%) pairs of <it>S. aureus </it>isolates obtained from clinically and subclinically affected mammary glands of the same ewe were indistinguishable by PFGE. The number of identical pairs was significantly greater than expected, based on the distribution of different <it>S. aureus </it>types within the flocks. One-third of the cases occurred during the first week after lambing, while a second peak was observed in the third week of lactation. Gangrene was present in 8.8% of the clinically affected glands; <it>S. aureus </it>was recovered from 72.9%, <it>Clostridium perfringens </it>from 6.3% and <it>E. coli </it>from 6.3% of the secretions from such glands. This study shows that <it>S. aureus </it>predominates as a cause of clinical ovine mastitis in Norway, also in very severe cases. Results also indicate that <it>S. aureus </it>is frequently spread between udder halves of infected ewes.</p

Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research and practice

As far back as the industrial revolution, great leaps in technical innovation succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision making engendering new opportunities for continued innovation. The impact of AI is significant, with industries ranging from: finance, retail, healthcare, manufacturing, supply chain and logistics all set to be disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: technological, business and management, science and technology, government and public sector. The research offers significant and timely insight to AI technology and its impact on the future of industry and society in general

Absence of DOA Effect but No Proper Test of the Lumberjack Effect: A Reply to Jamieson and Skraaning (2019)

This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.Objective: The aim was to evaluate the relevance of the critique offered by Jamieson and Skraaning (2019) regarding the applicability of the lumberjack effect of human–automation interaction to complex real-world settings. Background: The lumberjack effect, based upon a meta-analysis, identifies the consequences of a higher degree of automation—to improve performance and reduce workload—when automation functions as intended, but to degrade performance more, as mediated by a loss of situation awareness (SA) when automation fails. Jamieson and Skraaning provide data from a process control scenario that they assert contradicts the effect. Approach: We analyzed key aspects of their simulation, measures, and results which we argue limit the strength of their conclusion that the lumberjack effect is not applicable to complex real-world systems. Results: Our analysis revealed limits in their inappropriate choice of automation, the lack of a routine performance measure, support for the lumberjack effect that was actually provided by subjective measures of the operators, an inappropriate assessment of SA, and a possible limitation of statistical power. Conclusion: We regard these limitations as reasons to temper the strong conclusions drawn by the authors, of no applicability of the lumberjack effect to complex environments. Their findings should be used as an impetus for conducting further research on human–automation interaction in these domains. Applications: The collective findings of both Jamieson and Skraaning and our study are applicable to system designers and users in deciding upon the appropriate level of automation to deploy