Trust in Artificial Intelligence: Meta-Analytic Findings

2021 ◽  
pp. 001872082110139
Author(s):  
Alexandra D. Kaplan ◽  
Theresa T. Kessler ◽  
J. Christopher Brill ◽  
P. A. Hancock
Keyword(s):  
Artificial Intelligence ◽  
Empirical Research ◽  
Meta Analysis ◽  
Automated Vehicles ◽  
Factors Influencing ◽  
Moderating Factors ◽  
Shared Context ◽  
Build Systems ◽  
Significant Factors

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

scholarly journals Do M&A deals create or destroy value? A meta-analysis.

2016 ◽  
Vol 11 (2) ◽  
Author(s):  
Reinhard Meckl ◽  
Falk Röhrle
Keyword(s):  
Empirical Research ◽  
Positive Impact ◽  
Meta Analysis ◽  
Empirical Studies ◽  
Analytical Techniques ◽  
Time Frame ◽  
Moderator Analysis ◽  
Factors Influencing ◽  
A Company

Empirical research on the effect of M&A transactions on companies’ performance has not shown clear results of success. It is often assumed that these transactions destroy rather than create value. This study employs meta-analytical techniques to evaluate the outcomes of M&A transactions empirically. This method allows a large quantity of transactions to be examined. Additional factors influencing the performance of M&A transactions are found using a moderator analysis. In total, 55,399 transactions between 1950 and 2010, extracted from 33 previous M&A studies, have been examined. The results of this study confirm findings from previous empirical studies, stating that M&A transactions predominantly do not have a positive impact on the success of a company. A moderator analysis indicates that the type of M&A and the time frame used for measurement influence the success of M&A transactions.

Which Data to Meta-Analyze, and How?

2019 ◽  
Vol 227 (1) ◽  
pp. 64-82 ◽  
Author(s):  
Martin Voracek ◽  
Michael Kossmeier ◽  
Ulrich S. Tran
Keyword(s):  
Empirical Research ◽  
Meta Analysis ◽  
Curve Analysis ◽  
Research Progress ◽  
Primary Data ◽  
Statistical Procedures ◽  
Graphical Displays ◽  
Primary Data Analysis ◽  
Meta Analyses ◽  
Data Analytic

Abstract. Which data to analyze, and how, are fundamental questions of all empirical research. As there are always numerous flexibilities in data-analytic decisions (a “garden of forking paths”), this poses perennial problems to all empirical research. Specification-curve analysis and multiverse analysis have recently been proposed as solutions to these issues. Building on the structural analogies between primary data analysis and meta-analysis, we transform and adapt these approaches to the meta-analytic level, in tandem with combinatorial meta-analysis. We explain the rationale of this idea, suggest descriptive and inferential statistical procedures, as well as graphical displays, provide code for meta-analytic practitioners to generate and use these, and present a fully worked real example from digit ratio (2D:4D) research, totaling 1,592 meta-analytic specifications. Specification-curve and multiverse meta-analysis holds promise to resolve conflicting meta-analyses, contested evidence, controversial empirical literatures, and polarized research, and to mitigate the associated detrimental effects of these phenomena on research progress.

Ethics of Artificial Intelligence in Transport

2020 ◽  
pp. 667-683
Author(s):  
Bryant Walker Smith
Keyword(s):  
Artificial Intelligence ◽  
Ethical Issues ◽  
Ethical Question ◽  
Power Dynamics ◽  
Automated Vehicles ◽  
Automated Driving ◽  
The U.S ◽  
Policy Solutions

This chapter highlights key ethical issues in the use of artificial intelligence in transport by using automated driving as an example. These issues include the tension between technological solutions and policy solutions; the consequences of safety expectations; the complex choice between human authority and computer authority; and power dynamics among individuals, governments, and companies. In 2017 and 2018, the U.S. Congress considered automated driving legislation that was generally supported by many of the larger automated-driving developers. However, this automated-driving legislation failed to pass because of a lack of trust in technologies and institutions. Trustworthiness is much more of an ethical question. Automated vehicles will not be driven by individuals or even by computers; they will be driven by companies acting through their human and machine agents. An essential issue for this field—and for artificial intelligence generally—is how the companies that develop and deploy these technologies should earn people’s trust.

scholarly journals Systematic review protocol to assess artificial intelligence diagnostic accuracy performance in detecting acute ischaemic stroke and large-vessel occlusions on CT and MR medical imaging

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e043665
Author(s):  
Srinivasa Rao Kundeti ◽  
Manikanda Krishnan Vaidyanathan ◽  
Bharath Shivashankar ◽  
Sankar Prasad Gorthi
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Diagnostic Accuracy ◽  
Ischaemic Stroke ◽  
Study Protocol ◽  
Systematic Reviews ◽  
Acute Ischaemic Stroke ◽  
Meta Analysis ◽  
Large Vessel ◽  
Sufficient Data

IntroductionThe use of artificial intelligence (AI) to support the diagnosis of acute ischaemic stroke (AIS) could improve patient outcomes and facilitate accurate tissue and vessel assessment. However, the evidence in published AI studies is inadequate and difficult to interpret which reduces the accountability of the diagnostic results in clinical settings. This study protocol describes a rigorous systematic review of the accuracy of AI in the diagnosis of AIS and detection of large-vessel occlusions (LVOs).Methods and analysisWe will perform a systematic review and meta-analysis of the performance of AI models for diagnosing AIS and detecting LVOs. We will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols guidelines. Literature searches will be conducted in eight databases. For data screening and extraction, two reviewers will use a modified Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. We will assess the included studies using the Quality Assessment of Diagnostic Accuracy Studies guidelines. We will conduct a meta-analysis if sufficient data are available. We will use hierarchical summary receiver operating characteristic curves to estimate the summary operating points, including the pooled sensitivity and specificity, with 95% CIs, if pooling is appropriate. Furthermore, if sufficient data are available, we will use Grading of Recommendations, Assessment, Development and Evaluations profiler software to summarise the main findings of the systematic review, as a summary of results.Ethics and disseminationThere are no ethical considerations associated with this study protocol, as the systematic review focuses on the examination of secondary data. The systematic review results will be used to report on the accuracy, completeness and standard procedures of the included studies. We will disseminate our findings by publishing our analysis in a peer-reviewed journal and, if required, we will communicate with the stakeholders of the studies and bibliographic databases.PROSPERO registration numberCRD42020179652.

scholarly journals Factors Affecting Secondary Traumatic Stress of Nurses Caring for COVID-19 Patients in South Korea

2021 ◽  
Vol 18 (13) ◽  
pp. 6843
Author(s):  
Mee Sun Lee ◽  
Sujin Shin ◽  
Eunmin Hong
Keyword(s):  
Social Support ◽  
Regression Analysis ◽  
Traumatic Stress ◽  
Secondary Traumatic Stress ◽  
Direct Care ◽  
Hierarchical Regression ◽  
Factors Affecting ◽  
Factors Influencing ◽  
Affect Patient Care ◽  
Significant Factors

The secondary traumatic stress (STS) of nurses caring for COVID-19 patients is expected to be high, and it can adversely affect patient care. The purpose of this study was to examine the degree of STS of nurses caring for COVID-19 patients, and we identified various factors that influence STS. This study followed a descriptive design. The data of 136 nurses who had provided direct care to COVID-19 patients from 5 September to 26 September 2020 were collected online. Hierarchical regression analysis was conducted to identify the factors influencing STS. Participants experienced moderate levels of STS. The regression model of Model 1 was statistically significant (F = 6.21, p < 0.001), and the significant factors influencing STS were the duration of care for patients with COVID-19 for more than 30 days (β = 0.28, p < 0.001) and working in an undesignated COVID-19 hospital (β = 0.21, p = 0.038). In Model 2, the factor influencing STS was the support of a friend in the category of social support (β = −0.21, p = 0.039). The nurses caring for COVID-19 patients are experiencing a persistent and moderate level of STS. This study can be used as basic data to treat and prevent STS.

scholarly journals Individual Condensation Trails in Aircraft Trajectory Optimization

2019 ◽  
Vol 11 (21) ◽  
pp. 6082 ◽  
Author(s):  
Judith Rosenow ◽  
Hartmut Fricke
Keyword(s):  
Global Warming ◽  
Trajectory Optimization ◽  
Wind Shear ◽  
Impact Factors ◽  
Factors Influencing ◽  
Order Of Magnitude ◽  
Aircraft Trajectory ◽  
The Impact ◽  
Arbitrary Trajectory ◽  
Significant Factors

Contrails are one of the driving contributors to global warming, induced by aviation. The quantification of the impact of contrails on global warming is nontrivial and requires further in-depth investigation. In detail, condensation trails might even change the algebraic sign between a cooling and a warming effect in an order of magnitude, which is comparable to the impact of aviation-emitted carbon dioxides and nitrogen oxides. This implies the necessity to granularly consider the environmental impact of condensation trails in single-trajectory optimization tools. The intent of this study is the elaboration of all significant factors influencing on the net effect of single condensation trails. Possible simplifications will be proposed for a consideration in single-trajectory optimization tools. Finally, the effects of the most important impact factors, such as latitude, time of the year, and time of the day, wind shear, and atmospheric turbulence as well as their consideration in a multi-criteria trajectory optimization tool are exemplified. The results can be used for an arbitrary trajectory optimization tool with environmental optimization intents.

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