Journal articles: 'Trust in machines'

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Cascio, Jamais. "In machines we trust." New Scientist 229, no. 3064 (March 2016): 26–27. http://dx.doi.org/10.1016/s0262-4079(16)30413-4.

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Lee, Jieun, Yusuke Yamani, and Makoto Itoh. "Revisiting Trust in Machines: Examining Human–Machine Trust Using a Reprogrammed Pasteurizer Task." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 1767–70. http://dx.doi.org/10.1177/1541931218621400.

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Automated technologies have brought a number of benefits to professional domains, expanding the area in which humans can perform optimally in complex work environments. Human–automation trust has become an important aspect when designing acceptable automated systems considering general users who have no comprehensive knowledge of the systems. Muir and Moray (1996) proposed a model of human–machine trust incorporating predictability, dependability, and faith as predictors of overall trust in machines. Though Muir and Moray (1996) predicted that trust in machines grows from predictability, then dependability, and finally faith, their results suggested the opposite. This study will reexamine their theoretical framework and test which of the three dimensions governs initial trust in automation. Participants will be trained to operate a simulated pasteurization plant, as in Muir and Moray (1996), and they will be asked to maximize system performance in the pasteurizing task. We hypothesized that faith governs overall trust early in the interaction with the automated system, then dependability, and finally predictability as lay automation users become more familiar with the system. We attempt to replicate the results of Muir and Moray (1996) and argue that their model should be revised for trust development for general automation users.

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Stanley, Jeff, Ozgur Eris, and Monika Lohani. "A Conceptual Framework for Machine Self-Presentation and Trust." International Journal of Humanized Computing and Communication 2, no. 1 (March 1, 2021): 20–45. http://dx.doi.org/10.35708/hcc1869-148366.

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Increasingly, researchers are creating machines with humanlike social behaviors to elicit desired human responses such as trust and engagement, but a systematic characterization and categorization of such behaviors and their demonstrated effects is missing. This paper proposes a taxonomy of machine behavior based on what has been experimented with and documented in the literature to date. We argue that self-presentation theory, a psychosocial model of human interaction, provides a principled framework to structure existing knowledge in this domain and guide future research and development. We leverage a foundational human self-presentation taxonomy (Jones and Pittman, 1982), which associates human verbal behaviors with strategies, to guide the literature review of human-machine interaction studies we present in this paper. In our review, we identified 36 studies that have examined human-machine interactions with behaviors corresponding to strategies from the taxonomy. We analyzed frequently and infrequently used strategies to identify patterns and gaps, which led to the adaptation of Jones and Pittman’s human self-presentation taxonomy to a machine self-presentation taxonomy. The adapted taxonomy identifies strategies and behaviors machines can employ when presenting themselves to humans in order to elicit desired human responses and attitudes. Drawing from models of human trust we discuss how to apply the taxonomy to affect perceived machine trustworthiness.

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Mebane Jr., W. R. "POLITICAL SCIENCE: Can We Trust the Machines?" Science 322, no. 5902 (October 31, 2008): 677a—678a. http://dx.doi.org/10.1126/science.1165818.

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Willis-Owen, C. A. "Don't place all your trust in machines." BMJ 327, no. 7423 (November 8, 2003): 1084. http://dx.doi.org/10.1136/bmj.327.7423.1084.

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Anitha, H. M., and P. Jayarekha. "An Software Defined Network Based Secured Model for Malicious Virtual Machine Detection in Cloud Environment." Journal of Computational and Theoretical Nanoscience 17, no. 1 (January 1, 2020): 526–30. http://dx.doi.org/10.1166/jctn.2020.8481.

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Cloud computing is an emerging technology that offers the services to all the users as per their demand. Services are leveraged according to the Service level agreement (SLA). Service level agreement is monitored so that services are offered to the users without any problem and deprival. Software Defined Network (SDN) is used in order to monitor the trust score of the deployed Virtual Machines (VM) and Quality of Service (QoS) parameters offered. Software Defined Network controller is used to compute the trust score of the Virtual Machines and find whether Virtual Machine is malicious or trusted. Genetic algorithm is used to find the trusted Virtual Machine and release the resources allocated to the malicious Virtual Machine. This monitored information is intimated to cloud provider for further action. Security is enhanced by avoiding attacks from the malicious Virtual Machine in the cloud environment. The main objective of the paper is to enhance the security in the system using Software Defined Network based secured model.

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Quinn, Daniel B., Richard Pak, and Ewart J. de Visser. "Testing the Efficacy of Human-Human Trust Repair Strategies with Machines." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 1794–98. http://dx.doi.org/10.1177/1541931213601930.

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Trust is a critical component to both human-automation and human-human interactions. Interface manipulations, such as visual anthropomorphism and machine politeness, have been used to affect trust in automation. However, these design strategies are meant to primarily facilitate initial trust formation but have not been examined as a means to actively repair trust that has been violated by a system failure. Previous research has shown that trust in another party can be effectively repaired after a violation using various strategies, but there is little evidence substantiating such strategies in human-automation context. The current study will examine the effectiveness of trust repair strategies, derived from a human-human or human-organizational context, in human-automation interaction.

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Swanson, LaTasha R., Jennica L. Bellanca, and Justin Helton. "Automated Systems and Trust: Mineworkers' Trust in Proximity Detection Systems for Mobile Machines." Safety and Health at Work 10, no. 4 (December 2019): 461–69. http://dx.doi.org/10.1016/j.shaw.2019.09.003.

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Andras, Peter, Lukas Esterle, Michael Guckert, The Anh Han, Peter R. Lewis, Kristina Milanovic, Terry Payne, et al. "Trusting Intelligent Machines: Deepening Trust Within Socio-Technical Systems." IEEE Technology and Society Magazine 37, no. 4 (December 2018): 76–83. http://dx.doi.org/10.1109/mts.2018.2876107.

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Madhavan, Poornima, and Douglas A. Wiegmann. "A New Look at the Dynamics of Human-Automation Trust: Is Trust in Humans Comparable to Trust in Machines?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 48, no. 3 (September 2004): 581–85. http://dx.doi.org/10.1177/154193120404800365.

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Gabrieli, Giulio, Sarah Ng, and Gianluca Esposito. "Hacking Trust: The Presence of Faces on Automated Teller Machines (ATMs) Affects Trustworthiness." Behavioral Sciences 11, no. 6 (June 21, 2021): 91. http://dx.doi.org/10.3390/bs11060091.

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Trustworthiness is a core concept that drives individuals’ interaction with others, as well with objects and digital interfaces. The perceived trustworthiness of strangers from the evaluation of their faces has been widely studies in social psychology; however, little is known about the possibility of transferring trustworthiness from human faces to other individuals, objects or interfaces. In this study, we explore how the perceived trustworthiness of automated teller machines (ATMs) is influenced by the presence of faces on the machines, and how the trustworthiness of the faces themselves is transferred to the machine. In our study, participants (N = 57) rated the trustworthiness of ATMs on which faces of different age, gender, and ethnicity are placed. Subsequently, the trustworthiness of the ATMs is compared to the trustworthiness ratings of faces presented on their own. Results of our works support the idea that faces’ trustworthiness can be transferred to objects on which faces are presented. Moreover, the trustworthiness of ATMs seems to be influenced by the age of presented faces, with ATMs on which children faces are presented are trusted more than the same machines when adults’ or elders’ faces are presented, but not by the ethnicity (Asian or Caucasian) or gender (male or female) of presented faces.

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Hendrickx, Kilian, Wannes Meert, Bram Cornelis, Konstantinos Gryllias, and Jesse Davis. "Similarity-based anomaly score for fleet-based condition monitoring." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 9. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1178.

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An increased number of industrial assets are monitored during their daily use, producing large amounts of data. This data allows us to better monitor the health status of these asset, enabling predictive maintenance to reduce risks and costs caused by unexpected machine failure. Many condition monitoring approaches focus on assessing a machine's health status individually. Often, these approaches require historical data sets or handcrafted fault indicators. However, multiple industrial applications involve monitoring multiple similar operating machines, a fleet. By assuming the healthy behavior for the majority of the machine, deviating signatures can indicate a machine fault. In this work, we extend our previous proposed framework for fleet-based condition monitoring (Hendrickx et al.). This framework uses interpretable machine learning techniques to automatically evaluate assets within a fleet while incorporating domain knowledge if available. It is designed with four building blocks. In the first block, the user defines a similarity measure to compare machines. This measure can be both data-driven as based on domain knowledge. The second block clusters the machines based on this similarity measure. The third block assesses the health status of a machine by assigning an anomaly score where higher scores represent more deviating behavior. Finally, each of these blocks is visualized in the fourth block to guide a domain expert to set up and gain trust in the framework. The anomaly score proposed in our previous work has two shortcomings. First, its value can change very abruptly; a slight deviation can cause a machine's anomaly score to change from very low to very high. Second, the score does not accurately represent the anomalousness of a machine. A machine with the highest anomaly score is not necessarily the most deviating. Finally, the anomaly score is assigned to a group of machines. It is thus hard to assess the health status of an individual machine. As a consequence, this anomaly score offers little insights into a machine's performance. The contribution of this paper is a new implementation of the anomaly score block. Instead of basing our anomaly score on the clustering, we make use of the machine's similarities within the fleet. This solves the shortcomings of the previous anomaly score and defines an individualized, continuous scoring mechanism that represents the anomalousness of a machine. Hendrickx, Kilian, et al. “A General Anomaly Detection Framework for Fleet-Based Condition Monitoring of Machines.” Mechanical Systems and Signal Processing, vol. 139, Elsevier Ltd, 2019, p. 106585, doi:10.1016/j.ymssp.2019.106585.

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Usman, Aminu Bello, William Liu, Quan Bai, and Ajit Narayanan. "Trust of the Same." International Journal of Information Security and Privacy 9, no. 2 (April 2015): 13–30. http://dx.doi.org/10.4018/ijisp.2015040102.

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Trust and reputation management represents a significant trend in tackling the emerging security problems in computer networks. The basic idea is to let machines rate each other and then use the aggregated ratings to derive trust scores. Homophily i.e., love of the same, is the tendency of individuals to associate and bond with similar others mentioned in the social network, and the authors have discovered its presence, in term of nodes' attributes, in studying trust and reputation behaviors for the P2P oriented next generation of WSN. The simulation studies have confirmed the structural homophily, i.e., the similar way of connecting other nodes, is fostering trust characteristics and connections among peers.

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Sinenko, Sergei, Boris Zhadanovsky, and Vyacheslav Obodnikov. "Construction machines requirement for the building site." E3S Web of Conferences 110 (2019): 01036. http://dx.doi.org/10.1051/e3sconf/201911001036.

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Economic indicators of construction production are directly related to the choice of the optimal number of machines. The lack of machines for the production of construction and installation works leads to the impossibility of their implementation in the directive period. The pace of construction and installation work and labor productivity depend, to a large extent, on the degree of provision of construction facilities with machines (level of labor mechanization). The article considers the calculations of the need for construction machines for the building site, trust and the ministry. When determining the need for machines for construction sites, it is necessary to take into account ensuring the execution of works in the planned timeframe, increasing the level of complex mechanization, increasing labor productivity and reducing manual work, improving the use of the machine park and their operation. The article presents a consistent set of formulas that must be used when choosing construction machines and determining their number.

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Alhaji, Basel, Janine Beecken, Rüdiger Ehlers, Jan Gertheiss, Felix Merz, Jörg P. Müller, Michael Prilla, et al. "Engineering Human–Machine Teams for Trusted Collaboration." Big Data and Cognitive Computing 4, no. 4 (November 23, 2020): 35. http://dx.doi.org/10.3390/bdcc4040035.

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The way humans and artificially intelligent machines interact is undergoing a dramatic change. This change becomes particularly apparent in domains where humans and machines collaboratively work on joint tasks or objects in teams, such as in industrial assembly or disassembly processes. While there is intensive research work on human–machine collaboration in different research disciplines, systematic and interdisciplinary approaches towards engineering systems that consist of or comprise human–machine teams are still rare. In this paper, we review and analyze the state of the art, and derive and discuss core requirements and concepts by means of an illustrating scenario. In terms of methods, we focus on how reciprocal trust between humans and intelligent machines is defined, built, measured, and maintained from a systems engineering and planning perspective in literature. Based on our analysis, we propose and outline three important areas of future research on engineering and operating human–machine teams for trusted collaboration. For each area, we describe exemplary research opportunities.

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Christensen, James C., and Joseph B. Lyons. "Trust between Humans and Learning Machines: Developing the Gray Box." Mechanical Engineering 139, no. 06 (June 1, 2017): S9—S13. http://dx.doi.org/10.1115/1.2017-jun-5.

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This article explores the notion of the ‘Gray Box’ to symbolize the idea of providing sufficient information about the learning technology to establish trust. The term system is used throughout this article to represent an intelligent agent, robot, or other form of automation that possesses both decision initiative and authority to act. The article also discusses a proposed and tested Situation Awareness-based Agent Transparency (SAT) model, which posits that users need to understand the system’s perception, comprehension, and projection of a situation. One of the key challenges is that a learning system may adopt behavior that is difficult to understand and challenging to condense to traditional if-then statements. Without a shared semantic space, the system will have little basis for communicating with the human. One of the key recommendations of this article is that there is a need to provide learning systems with transparency as to the state of the human operator, including their momentary capabilities and potential impact of changes in task allocation and teaming approach.

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Lopez, Jeremy, and Richard Pak. "Trust Strategies in Consumer Multiple-Component Systems." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 1756–60. http://dx.doi.org/10.1177/1541931218621397.

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Trust is a critical factor in successful and productive human-automation interactions. Human trust in machines is sensitive to machine performance. When automation malfunctions, trust is negatively affected. The development of increasingly complex multiple-component systems, or those with several autonomous elements, introduces even more ways for a system to err. One example is in smart home control systems where different subsystems may be controlled by different autonomous routines or rules. Multiple studies suggest that one error-prone component can lower user trust in the remaining components. This has been termed a “pull down effect.” Other research suggests that increasing the amount of information presented to the user can reduce the strength of the pull down effect by promoting heterogeneity of components. Although a majority of this research has been tested in the industrial domain, there exist certain types of information that are best suited for consumer automation (e.g., granting the automation a name and a voice). Providing this kind of information to the user may diminish the strength of the pull down effect. Thus, the current study will investigate the effectiveness of trust-preserving heterogeneity strategies in consumer multiple-component systems.

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Arora, Shakti, and Surjeet Dalal. "Trust Evaluation Factors in Cloud Computing with Open Stack." Journal of Computational and Theoretical Nanoscience 16, no. 12 (December 1, 2019): 5073–77. http://dx.doi.org/10.1166/jctn.2019.8566.

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Cloud computing can be used with three deploying models SAAS, IAAS, and PAAS. IAAS is the most adopted model used for research and innovations. On daily basis Tera bytes of data are being uploaded on the cloud. The Public cloud, Private cloud and hybrid clouds services are used by the people for hosting their applications and computations. One of the major obstacle used in selection of cloud service providers is the lack of trust on CSPs. CSPs are providing different levels of security according to the requirements of users. Proposed paper demonstrates the Trust evaluation, Trust calculation models, based on different security parameters like Turnaround time, Integrity, and reliability of data on cloud. How to select a cloud provider’s service, based on the derived parameters determines the trust value of service provider. A new dynamic trust calculation approach is proposed in this paper. Integrity, Turnaround time (TE) and reliability parameters are considered for trust evaluation. Comparative analysis of different cloud providers with real time calculations of different cloud machines are done and made a comparative analysis of the parameter, approximately 63% efficiency level is achieved while compiling the trust factor with standard cloud nodes. Open stack cloud software is used to create a cloud environment of the local machine which helps in compilation of the data at real time. Fog computing is used to calculate the results.

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Yan, Ling Yun, Ding Kuan Ye, Xin Min Zhang, Xun Shen, and Yun Yue Ye. "Analysis and Optimization of Starting Characteristics of Tubular Linear Induction Motor." Applied Mechanics and Materials 416-417 (September 2013): 322–27. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.322.

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Traditional mechanical punching machines are mainly driven by rotary motors and a complete set of intermediate transformation mechanism is needed, which results in a reduction of the system efficiency. This paper focuses on the tubular linear induction motor (TLIM) with low cost and simple control structure which directly drives the punching machines. In order to provide a theoretical basis for its further control system, the finite element model of this TLIM is established. The starting performances, such as the trust force, phase current, velocity, displacement and the load force, are analyzed by the finite element analysis (FEA). Moreover, the optimization of the starting trust force is investigated.

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Muir, Bonnie M. "Trust between humans and machines, and the design of decision aids." International Journal of Man-Machine Studies 27, no. 5-6 (November 1987): 527–39. http://dx.doi.org/10.1016/s0020-7373(87)80013-5.

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Pesudovs, Konrad, and Raymond A. Applegate. "Data quality and clinical decision-making: do we trust machines blindly?" Clinical and Experimental Optometry 92, no. 3 (May 2009): 173–75. http://dx.doi.org/10.1111/j.1444-0938.2009.00367.x.

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Moray, Neville, and T. Inagaki. "Laboratory studies of trust between humans and machines in automated systems." Transactions of the Institute of Measurement and Control 21, no. 4-5 (October 1999): 203–11. http://dx.doi.org/10.1177/014233129902100408.

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M. Mousa, Hamdy, and Gamal F. Elhady. "Trust Model Development for Cloud Environment using Fuzzy Mamdani and Simulators." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 11 (November 30, 2014): 5142–54. http://dx.doi.org/10.24297/ijct.v13i11.2784.

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Nowadays, Cloud computing is an expanding area in research and industry, which involves virtualization, distributed computing, internet, software, security, web services and etc. A cloud consists of several elements such as clients, data centers and distributed servers, internet and it includes fault tolerance, high availability, effectiveness, scalability, flexibility, reduced overhead for users, reduced cost of ownership, on demand services and etc. Now the next factor is coming, cost of Virtual machines on Data centers and response time. So this paper develop trust model in cloud computing based on fuzzy logic, to explores the coordination between Data Centers and user bound to optimize the application performance, cost of Virtual machines on Data centers and response time using Cloud Computing Analyst.

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V. Samuel Blessed Nayagam, P., and A. Shajin Nargunam. "Secure Data Verification and Virtual Machine Monitoring." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 574. http://dx.doi.org/10.14419/ijet.v7i4.36.24140.

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Powerfully configurable virtualized assets make the physical area of the information and process autonomous of its portrayal and the clients have no influence over the physical arrangement of information and running procedure. In a multi-cloud condition the layer of deliberation between the physical equipment and virtualized frameworks gives a great way to convey cost reserve funds through server union and also expanded operational productivity and adaptability. This additional usefulness presents a virtualization layer that it turns into a chance of assault for the facilitated virtual administrations. The proposed access control show ensures virtual machines by receiving access control at various layers. The information shading plan help to secure the virtualized information utilized in the virtual machines. The information confirmation structure, which gives a grouping of trust wipes out the untrusted special virtual machines, and additionally utilize the confided in processing standards to guarantee the respectability of the checking condition. Safeguarding security plot ceaselessly screens the working and trade of information between the virtual machine. The test results demonstrate that this plan can viably counteract virtual machine escape without influencing the general productivity of the framework.

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Kaplan, A. D., T. T. Kessler, and P. A. Hancock. "How Trust is Defined and its use in Human-Human and Human-Machine Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1150–54. http://dx.doi.org/10.1177/1071181320641275.

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Trust is a critical element in virtually all forms of interactions, including those between humans and machines. Yet aspects of trust do vary somewhat between definitions. The present work seeks to unify these disparate definitions of trust, comparing and contrasting between the major works. Overall, every definition of trust involves an individual in a position of vulnerability (the trustor) and a person on whom they must rely (the trustee) despite circumstances which may place the trustor in some kind of potential for harm. Such engagements are enacted in order to secure some form of gain from the trusting relationship. The ways in which these definitions influence empirical measurement (both qualitative and quantitative) are identified and elaborated on.

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Castelo, Noah, Maarten W. Bos, and Donald Lehmann. "Let the Machine Decide: When Consumers Trust or Distrust Algorithms." NIM Marketing Intelligence Review 11, no. 2 (November 1, 2019): 24–29. http://dx.doi.org/10.2478/nimmir-2019-0012.

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AbstractThanks to the rapid progress in the field of artificial intelligence algorithms are able to accomplish an increasingly comprehensive list of tasks, and often they achieve better results than human experts. Nevertheless, many consumers have ambivalent feelings towards algorithms and tend to trust humans more than they trust machines. Especially when tasks are perceived as subjective, consumers often assume that algorithms will be less effective, even if this belief is getting more and more inaccurate.To encourage algorithm adoption, managers should provide empirical evidence of the algorithm’s superior performance relative to humans. Given that consumers trust in the cognitive capabilities of algorithms, another way to increase trust is to demonstrate that these capabilities are relevant for the task in question. Further, explaining that algorithms can detect and understand human emotions can enhance adoption of algorithms for subjective tasks.

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Edmonds, Mark, Feng Gao, Hangxin Liu, Xu Xie, Siyuan Qi, Brandon Rothrock, Yixin Zhu, Ying Nian Wu, Hongjing Lu, and Song-Chun Zhu. "A tale of two explanations: Enhancing human trust by explaining robot behavior." Science Robotics 4, no. 37 (December 18, 2019): eaay4663. http://dx.doi.org/10.1126/scirobotics.aay4663.

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The ability to provide comprehensive explanations of chosen actions is a hallmark of intelligence. Lack of this ability impedes the general acceptance of AI and robot systems in critical tasks. This paper examines what forms of explanations best foster human trust in machines and proposes a framework in which explanations are generated from both functional and mechanistic perspectives. The robot system learns from human demonstrations to open medicine bottles using (i) an embodied haptic prediction model to extract knowledge from sensory feedback, (ii) a stochastic grammar model induced to capture the compositional structure of a multistep task, and (iii) an improved Earley parsing algorithm to jointly leverage both the haptic and grammar models. The robot system not only shows the ability to learn from human demonstrators but also succeeds in opening new, unseen bottles. Using different forms of explanations generated by the robot system, we conducted a psychological experiment to examine what forms of explanations best foster human trust in the robot. We found that comprehensive and real-time visualizations of the robot’s internal decisions were more effective in promoting human trust than explanations based on summary text descriptions. In addition, forms of explanation that are best suited to foster trust do not necessarily correspond to the model components contributing to the best task performance. This divergence shows a need for the robotics community to integrate model components to enhance both task execution and human trust in machines.

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Pintér, Róbert. "A mesterséges intelligencia nyomában – Konferenciabeszámolók." Információs Társadalom 19, no. 1 (November 26, 2019): 138. http://dx.doi.org/10.22503/inftars.xix.2019.1.8.

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A mesterséges intelligencia nyomában – Konferenciabeszámolók Beszámoló a V4 Conference on Artificial Intelligence (2018. október 11., Brüsszel, Belgium), a Making AI at Google (2018. november 6–7., Amszterdam, Hollandia)és a The Aspen Institute Central Europe, PUBLIC DEBATE: Beyond Human. Trust in Machines and AI és WORKSHOP: Building the Future: Addressing the Opportunities and Challenges of an AI-Enabled World (2019. január 22–23., Prága, Csehország) rendezvényekről. --- In the footsteps of artificial intelligence – Conference reports Conference reports on V4 Conference on Artificial Intelligence (11 October 2018., Brussels, Belgium), the Making AI at Google (6-7 November 2018., Amsterdam, The Netherlands) and The Aspen Institute Central Europe, PUBLIC DEBATE: Beyond Human. Trust in Machines and AI és WORKSHOP: Building the Future: Addressing the Opportunities and Challenges of an AI-Enabled World (22-23 January 2019., Práague, Czechia).

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Han, The Anh, Cedric Perret, and Simon T. Powers. "When to (or not to) trust intelligent machines: Insights from an evolutionary game theory analysis of trust in repeated games." Cognitive Systems Research 68 (August 2021): 111–24. http://dx.doi.org/10.1016/j.cogsys.2021.02.003.

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Dhulekar, Komal, and Madhuri Devrankar. "A REVIEW ON SEMANTIC WEB." International Journal of Engineering Technologies and Management Research 6, no. 12 (April 3, 2020): 22–28. http://dx.doi.org/10.29121/ijetmr.v6.i12.2019.470.

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Semantic web is a concept that enables better machine processing of information on the web, by structuring documents written for the web in such a way that they become understandable by machines. This can be used for creating more complex applications (intelligent browsers, more advanced web agents), etc. Semantic modeling languages like the Resource Description Framework (RDF) and topic maps employ XML syntax to achieve this objective. New tools exploit cross domain vocabularies to automatically extract and relate the meta information in a new context. Web Ontology languages like DAML+OIL extend RDF with richer modeling primitives and a provide a technological basis to enable the Semantic Web. The logic languages for Semantic Web are described (which build on the of RDF and ontology languages). They, together with digital signatures, enable a web of trust, which will have levels of trust for its resources and for the rights of access, and will enable generating proofs, for the actions and resources on the web.

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Sharma, Oshin, and Hemraj Saini. "SLA and Performance Efficient Heuristics for Virtual Machines Placement in Cloud Data Centers." International Journal of Grid and High Performance Computing 9, no. 3 (July 2017): 17–33. http://dx.doi.org/10.4018/ijghpc.2017070102.

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Cloud computing has revolutionized the working models of IT industry and increasing the demand of cloud resources which further leads to increase in energy consumption of data centers. Virtual machines (VMs) are consolidated dynamically to reduce the number of host machines inside data centers by satisfying the customer's requirements and quality of services (QoS). Moreover, for using the services of cloud environment every cloud user has a service level agreement (SLA) that deals with energy and performance trade-offs. As, the excess of consolidation and migration may degrade the performance of system, therefore, this paper focuses the overall performance of the system instead of energy consumption during the consolidation process to maintain a trust level between cloud's users and providers. In addition, the paper proposed three different heuristics for virtual machine (VM) placement based on current and previous usage of resources. The proposed heuristics ensure a high level of service level agreements (SLA) and better performance of ESM metric in comparison to previous research.

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Akash, Kumar, Wan-Lin Hu, Neera Jain, and Tahira Reid. "A Classification Model for Sensing Human Trust in Machines Using EEG and GSR." ACM Transactions on Interactive Intelligent Systems 8, no. 4 (November 22, 2018): 1–20. http://dx.doi.org/10.1145/3132743.

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Ryan, Mark. "In AI We Trust: Ethics, Artificial Intelligence, and Reliability." Science and Engineering Ethics 26, no. 5 (June 10, 2020): 2749–67. http://dx.doi.org/10.1007/s11948-020-00228-y.

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Abstract One of the main difficulties in assessing artificial intelligence (AI) is the tendency for people to anthropomorphise it. This becomes particularly problematic when we attach human moral activities to AI. For example, the European Commission’s High-level Expert Group on AI (HLEG) have adopted the position that we should establish a relationship of trust with AI and should cultivate trustworthy AI (HLEG AI Ethics guidelines for trustworthy AI, 2019, p. 35). Trust is one of the most important and defining activities in human relationships, so proposing that AI should be trusted, is a very serious claim. This paper will show that AI cannot be something that has the capacity to be trusted according to the most prevalent definitions of trust because it does not possess emotive states or can be held responsible for their actions—requirements of the affective and normative accounts of trust. While AI meets all of the requirements of the rational account of trust, it will be shown that this is not actually a type of trust at all, but is instead, a form of reliance. Ultimately, even complex machines such as AI should not be viewed as trustworthy as this undermines the value of interpersonal trust, anthropomorphises AI, and diverts responsibility from those developing and using them.

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Awad, Edmond, Jean-François Bonnefon, Azim Shariff, and Iyad Rahwan. "The Thorny Challenge of Making Moral Machines: Ethical Dilemmas with Self-Driving Cars." NIM Marketing Intelligence Review 11, no. 2 (November 1, 2019): 42–47. http://dx.doi.org/10.2478/nimmir-2019-0015.

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AbstractThe algorithms that control AVs will need to embed moral principles guiding their decisions in situations of unavoidable harm. Manufacturers and regulators are confronted with three potentially incompatible objectives: being consistent, not causing public outrage, and not discouraging buyers. The presented moral machine study is a step towards solving this problem as it tries to learn how people all over the world feel about the alternative decisions the AI of self-driving vehicles might have to make. The global study displayed broad agreement across regions regarding how to handle unavoidable accidents. To master the moral challenges, all stakeholders should embrace the topic of machine ethics: this is a unique opportunity to decide as a community what we believe to be right or wrong, and to make sure that machines, unlike humans, unerringly follow the agreed-upon moral preferences. The integration of autonomous cars will require a new social contract that provides clear guidelines about who is responsible for different kinds of accidents, how monitoring and enforcement will be performed, and how trust among all stakeholders can be engendered.

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Rakhi and G. L. Pahuja. "A Reliable Solution to Load Balancing with Trust Based Authentication Enhanced by Virtual Machines." International Journal of Information Technology and Computer Science 9, no. 11 (November 8, 2017): 64–71. http://dx.doi.org/10.5815/ijitcs.2017.11.07.

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Prahl, Andrew, and Lyn Van Swol. "Out with the Humans, in with the Machines?: Investigating the Behavioral and Psychological Effects of Replacing Human Advisors with a Machine." Human-Machine Communication 2 (January 15, 2021): 209–34. http://dx.doi.org/10.30658/hmc.2.11.

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This study investigates the effects of task demonstrability and replacing a human advisor with a machine advisor. Outcome measures include advice-utilization (trust), the perception of advisors, and decision-maker emotions. Participants were randomly assigned to make a series of forecasts dealing with either humanitarian planning (low demonstrability) or management (high demonstrability). Participants received advice from either a machine advisor only, a human advisor only, or their advisor was replaced with the other type of advisor (human/machine) midway through the experiment. Decision-makers rated human advisors as more expert, more useful, and more similar. Perception effects were strongest when a human advisor was replaced by a machine. Decision-makers also experienced more negative emotions, lower reciprocity, and faulted their advisor more for mistakes when a human was replaced by a machine.

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Preuveneers, Davy, Wouter Joosen, and Elisabeth Ilie-Zudor. "Trustworthy data-driven networked production for customer-centric plants." Industrial Management & Data Systems 117, no. 10 (December 4, 2017): 2305–24. http://dx.doi.org/10.1108/imds-10-2016-0419.

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Purpose Industry 4.0 envisions a future of networked production where interconnected machines and business processes running in the cloud will communicate with one another to optimize production and enable more efficient and sustainable individualized/mass manufacturing. However, the openness and process transparency of networked production in hyperconnected manufacturing enterprises pose severe cyber-security threats and information security challenges that need to be dealt with. The paper aims to discuss these issues. Design/methodology/approach This paper presents a distributed trust model and middleware for collaborative and decentralized access control to guarantee data transparency, integrity, authenticity and authorization of dataflow-oriented Industry 4.0 processes. Findings The results of a performance study indicate that private blockchains are capable of securing IoT-enabled dataflow-oriented networked production processes across the trust boundaries of the Industry 4.0 manufacturing enterprise. Originality/value This paper contributes a decentralized identity and relationship management for users, sensors, actuators, gateways and cloud services to support processes that cross the trust boundaries of the manufacturing enterprise, while offering protection against malicious adversaries gaining unauthorized access to systems, services and information.

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Chaouali, Walid, and Kamel El Hedhli. "Toward a contagion-based model of mobile banking adoption." International Journal of Bank Marketing 37, no. 1 (February 4, 2019): 69–96. http://dx.doi.org/10.1108/ijbm-05-2017-0096.

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Purpose The purpose of this paper is to address the following question: Can a bank capitalize on its well-established self-service technologies (SSTs) in order to entice customers to adopt a newly introduced SST, namely, mobile banking? More specifically, it proposes an integrative model that simultaneously investigates the transference effects of attitudes, trust and the contagious influences of social pressures on mobile banking adoption intentions. Design/methodology/approach Structural equation modeling is applied to data collected from banks’ clients who are actually non-users of mobile banking. Findings The results indicate that attitude toward and trust in mobile banking along with coercive, normative and mimetic pressures are key antecedents to mobile banking adoption intentions. In addition, attitudes toward automated teller machines (ATMs) and online banking significantly predict attitude toward mobile banking. The results also support the effects of trust in ATMs as well as trust in online banking on trust in mobile banking. Moreover, predicted differences in the relative effects of attitude and trust are supported. Particularly, attitude toward online banking has a stronger impact on attitude toward mobile banking compared to the impact of attitude toward ATMs. In the same vein, the effect of trust in online banking on mobile banking is significantly stronger than the effect of trust in ATMs. Practical implications The study’s results hint at some practical and worthwhile guidelines for banks that can be leveraged in communication campaigns aiming at boosting the adoption rates of mobile banking. Banks can take advantage of the transference effects of the established attitudes toward and trusting beliefs in their mature SSTs as well as the contagious social influences in inducing the adoption of a newly introduced SST. Originality/value The present study represents a first step toward generating new insights into the role of the joint effects of attitudes, trust and social influences in the adoption of a new SST.

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Ile, Cristina. "Analyze the Process Capability: A Case Study." Applied Mechanics and Materials 436 (October 2013): 311–17. http://dx.doi.org/10.4028/www.scientific.net/amm.436.311.

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A process is defined as an unique combination between materials, machines, methods and persons. It is necessary to do an analysis of the process capability in a company in order to be competitive and to gain the trust of the client. The study presents what means Process Capability and how it can be implemented in a wood based company. For this paper all the simulations were done in Minitab, but similar simulations can be done with other software programs.

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Awad, Mohammed, and Ernst L. Leiss. "Paper Records and Electronic Audits: A Step Towards Regaining Voter Trust." JeDEM - eJournal of eDemocracy and Open Government 2, no. 1 (April 25, 2010): 69–78. http://dx.doi.org/10.29379/jedem.v2i1.12.

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The shortcomings of the voting systems used in the 2000 presidential election raised the awareness of the need to replace these systems. As a result, with the funds of Help America Vote Act (HAVA), many US states switched to Direct Recording Electronic (DRE) voting systems before the 2004 elections. Unfortunately these paperless voting machines were not as secure and efficient as state officials had hoped. Since their implementation, many studies have shown the threats of these systems and their flaws. While some data security experts tried to improve these voting systems, many discouraged their use and recommended more transparent methods.Although, several countries in Europe and around the world have successfully utilized E-voting, many US districts and states still don’t trust this technology and are returning to paper ballots. In this paper, we will propose a new system as a solution to the current problems. This approach combines the advantages of both paper ballots and Direct Recording Electronic (DRE) voting systems while avoiding the major flaws of these systems.

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CASTELFRANCHI, CRISTIANO. "THE SOCIAL NATURE OF INFORMATION AND THE ROLE OF TRUST." International Journal of Cooperative Information Systems 11, no. 03n04 (September 2002): 381–403. http://dx.doi.org/10.1142/s0218843002000649.

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In this paper, I claim that "information" is a social construct, is created and tailored on purpose by somebody for somebody else or collectively, accepted, believed, or propagated through social interactions. In particular, I argue about the essential social nature of several crucial aspects of information in IT and specifically in relation to Information Agents. After situating this view of information within the broader perspective of Social Artificial Intelligence — the new AI paradigm — I analyize the intrinsic social aspects of information ontology, search and access, presentation, overload, credibility and sources, value. The issue of real "collaboration" in providing information deserves special attention and I introduce our theory of over-help and of the ability to provide something different from what is requested in order to satisfy the real need beyond the request. In the second part, I focus on the crucial role of trust (an intrinsically social and cognitive notion) for dealing with information and sources especially on the web. My objective is to claim that: if information per se is a social construct, then its technology should be socially designed and integrated, and that machines must be involved in real social relationships because they have to mediate them among humans. It is necessary incorporating some part of this social knowledge and capability in the information technology itself, especially in adaptive and interacting "agents" and MAS.

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Rasheed, Hafiz Muhammad Wasif, Junaid Khalid, Hafiz Muhammad Usman Khizar, Muhammad Sajid, Muhammad Naeem Shahid, Mehboob Ahmad, and Waseem Ahmad Khan. "Factors affecting Customer Loyalty in Banking Sector: A study on Banks in Bahawalpur (Pakistan)." International Journal of Accounting and Financial Reporting 1, no. 1 (June 1, 2015): 239. http://dx.doi.org/10.5296/ijafr.v5i1.7726.

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The emergence of new banking channels like as phone banking, Internet banking, Automated Teller Machines (ATM), furthermore developing financial market & global competition have constrained bankers to explore the importance of customer loyalty. For the banks to be successful in the intensively competitive environment, they are bound to attach importance to customer loyalty. The aim of this study is to investigate how the behavior and intentions of the respondents will be influenced by different factors of customer loyalty. The objectives of the current study are of two folds; firstly, to examine the relationship of trust, customer satisfaction and service quality with customer loyalty. Secondly, to determine which factor influenced customer loyalty the most? Data was collected through non probability convenience sampling, from a sample of 496 respondents by using a self-administered questionnaire. The collected Data was analyzed through regression technique using SPSS 17. Results of the current study reveal that trust, customer satisfaction and service quality has significant effect on customer loyalty. But the relationship or trust with customer satisfaction is not proved. Moreover the study revealed that customer satisfaction is found as the most influential variable for customer loyalty. The managerial implication of this research is the considerations of bank managers for the enhancement of their customer loyalty.

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Nowak, Magdalena, Yvonne Jeanes, and Sue Reeves. "The food environment in leisure centres and health clubs: how appropriate is it for children?" Nutrition & Food Science 42, no. 5 (September 5, 2012): 307–14. http://dx.doi.org/10.1108/00346651211266818.

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PurposeLeisure centres and health clubs are ideal places for promoting healthy lifestyle. They promote physical exercise and many activities for children, such as swimming, soft play areas, crèche, and team sports. The purpose of this paper is to evaluate the food environment for children in leisure centres and health clubs in London.Design/methodology/approachIn total, 67 venues were visited. All food and drink options were recorded and the proportion of “healthy” options was calculated according to the School Food Trust criteria and Nutrient Profiling Model.FindingsIn total, 96 per cent of the venues had vending machines and 51 per cent had onsite restaurants/cafés. According to The School Food Trust criteria, only 13 per cent of vending machine drinks, 77.2 per cent of meals, and 24 per cent of snacks would be allowed in school canteens.Research limitations/implicationsThe study revealed that a low proportion of healthy foods and drinks were offered to children in Leisure centres in London. However, the survey was only extended to venues in the capital.Practical implicationsThe results of the study suggest that new recommendations such as the Healthy Food Code of Good Practice, omitted leisure centres. The findings presented here could provide scientific evidence for campaigns and interventions aimed at improving the quality and the appropriateness of foods and drinks offered to children.Originality/valueThe paper shows that health campaigns and legislation should target leisure centres and health clubs, in order to improve the food and drinks facilities and promote healthy eating, particularly in light of the upcoming Olympic Games in London 2012.

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Choudhary, Karanjeet, Gurjot Singh Gaba, Ismail Butun, and Pardeep Kumar. "MAKE-IT—A Lightweight Mutual Authentication and Key Exchange Protocol for Industrial Internet of Things." Sensors 20, no. 18 (September 10, 2020): 5166. http://dx.doi.org/10.3390/s20185166.

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Continuous development of the Industrial Internet of Things (IIoT) has opened up enormous opportunities for the engineers to enhance the efficiency of the machines. Despite the development, many industry administrators still fear to use Internet for operating their machines due to untrusted nature of the communication channel. The utilization of internet for managing industrial operations can be widespread adopted if the authentication of the entities are performed and trust is ensured. The traditional schemes with their inherent security issues and other complexities, cannot be directly deployed to resource constrained network devices. Therefore, we have proposed a strong mutual authentication and secret key exchange protocol to address the vulnerabilities of the existing schemes. We have used various cryptography operations such as hashing, ciphering, and so forth, for providing secure mutual authentication and secret key exchange between different entities to restrict unauthorized access. Performance and security analysis clearly demonstrates that the proposed work is energy efficient (computation and communication inexpensive) and more robust against the attacks in comparison to the traditional schemes.

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Banegas-Luna, Antonio Jesús, Jorge Peña-García, Adrian Iftene, Fiorella Guadagni, Patrizia Ferroni, Noemi Scarpato, Fabio Massimo Zanzotto, Andrés Bueno-Crespo, and Horacio Pérez-Sánchez. "Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey." International Journal of Molecular Sciences 22, no. 9 (April 22, 2021): 4394. http://dx.doi.org/10.3390/ijms22094394.

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Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine—specifically, to cancer research—and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors’ predictive capacity and achieve individualised therapies in the near future.

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McCormack, Jon, Patrick Hutchings, Toby Gifford, Matthew Yee-King, Maria Teresa Llano, and Mark D’inverno. "Design Considerations for Real-Time Collaboration with Creative Artificial Intelligence." Organised Sound 25, no. 1 (March 4, 2020): 41–52. http://dx.doi.org/10.1017/s1355771819000451.

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Machines incorporating techniques from artificial intelligence and machine learning can work with human users on a moment-to-moment, real-time basis to generate creative outcomes, performances and artefacts. We define such systems collaborative, creative AI systems, and in this article, consider the theoretical and practical considerations needed for their design so as to support improvisation, performance and co-creation through real-time, sustained, moment-to-moment interaction. We begin by providing an overview of creative AI systems, examining strengths, opportunities and criticisms in order to draw out the key considerations when designing AI for human creative collaboration. We argue that the artistic goals and creative process should be first and foremost in any design. We then draw from a range of research that looks at human collaboration and teamwork, to examine features that support trust, cooperation, shared awareness and a shared information space. We highlight the importance of understanding the scope and perception of two-way communication between human and machine agents in order to support reflection on conflict, error, evaluation and flow. We conclude with a summary of the range of design challenges for building such systems in provoking, challenging and enhancing human creative activity through their creative agency.

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Chandrakala, N., and B. Thirumala Rao. "Migration of Virtual Machine to improve the Security of Cloud Computing." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 1 (February 1, 2018): 210. http://dx.doi.org/10.11591/ijece.v8i1.pp210-219.

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Cloud services help individuals and organization to use data that are managed by third parties or another person at remote locations. With the increase in the development of cloud computing environment, the security has become the major concern that has been raised more consistently in order to move data and applications to the cloud as individuals do not trust the third party cloud computing providers with their private and most sensitive data and information. This paper presents, the migration of virtual machine to improve the security in cloud computing. Virtual machine (VM) is an emulation of a particular computer system. In cloud computing, virtual machine migration is a useful tool for migrating operating system instances across multiple physical machines. It is used to load balancing, fault management, low-level system maintenance and reduce energy consumption. Virtual machine (VM) migration is a powerful management technique that gives data center operators the ability to adapt the placement of VMs in order to better satisfy performance objectives, improve resource utilization and communication locality, achieve fault tolerance, reduce energy consumption, and facilitate system maintenance activities. In the migration based security approach, proposed the placement of VMs can make enormous difference in terms of security levels. On the bases of survivability analysis of VMs and Discrete Time Markov Chain (DTMC) analysis, we design an algorithm that generates a secure placement arrangement that the guest VMs can moves before succeeds the attack.

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Iovanas, Razvan Florin, Daniela Maria Iovanas, and Adela Eliza Dumitrascu. "Estimating the Reliability for Asphalt Milling Machines Teeth Manufactured by Claddings Processes." Advanced Materials Research 1029 (September 2014): 88–93. http://dx.doi.org/10.4028/www.scientific.net/amr.1029.88.

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The issues from the last decades related to the crisis of natural resources, raw materials, equipment, energy etc., gave rise to a new concept, namely sustainable development. The theory of sustainable development focuses on the development of quality products to provide trust and confidence, so as reliable. On this line the paper presents an approach on assessing the reliability and non-reliability of the milling teeth for asphalt machines classic manufactured and those manufactured by welding load, which give them self wear protection systems and self-lock at rotation. So in this sense there are presented comparative estimates regarding to the operating lifetime of the four batches of milling teeth for asphalt, 41Cr4 mark steel (EN 10083-1) the classic version and 3 groups of teeth load by weld on their working surface(wear) by different welding methods, namely: WIG - with filler material type tubular wire FILEUR DUR 606B, WIG – filler material with tubular type VT2, 5CrTiD rods, and MIG/MAG (CMT), the tubular wire filler material type FILEUR DUR. So, asphalt milling machines were stripped with 4 groups of teeth in order to track the behaviour during exploitation of each batch. Assuming that the product specifications properly respond customers' requirements, the level of reliability can be measured accurately by the fraction of delivered units that meet the specifications. On this line for estimating the reliability the Monte Carlo simulation method was used, which is suitable for analyzing the products that are designed to provide superior quality.

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Verma, Prashant K. "Observations of MI Values During Neonatal Cardiac Ultrasound Scanning." Ultrasound 16, no. 4 (November 1, 2008): 203–7. http://dx.doi.org/10.1179/174313408x353828.

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Aim: The on-screen Mechanical Index (MI), was recorded from eight diagnostic ultrasound machines used for cardiac scanning of neonates. The objective of the study was to compare values of MI used in neonatal cardiac scanning with those recommended by the British Medical Ultrasound Society safety guidelines. Methods: The eight scanners were based at Sheffield Teaching Hospitals NHS Trust (STH), Sheffield Children's Hospitals NHS Trust (SCH) and Leeds Teaching Hospitals NHS Trust (LTH). Two methods of recording the MI value were used. At the Sheffield sites the on-screen safety index was obtained from archived patient studies saved on the Trust PACS. At the Leeds site the on-screen safety index was recorded from the scanner when it was switched on to its default neonatal or paediatric pre-sets with the appropriate transducer connected. Results: Data from clinical examinations at the Sheffield sites show that on average the MI values, for cardiac scanning, in B-mode and colour/pulsed wave Doppler mode were 1·1±0·3 and 1·3±0·3 respectively. The maximum values of MI recorded during clinical cardiac examinations were 1·6 for B-mode and 1·7 for colour/pulsed wave Doppler mode. Data from the Leeds site recorded default MI values, using appropriate transducers with neonatal and paediatric presets. One scanner from the Leeds site defaulted to an MI value of 1·9 in colour/pulsed Doppler mode with a neonatal preset. All the MI values are well above BMUS guideline recommendations for neonates when lung tissue is present within the ultrasound beam. Conclusions: The implication of these results is that neonates undergoing cardiac ultrasound examinations, were regularly exposed to MI values at least four to six times that recommended by BMUS guidelines (MI<0·3).

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Gennatas, Efstathios D., Jerome H. Friedman, Lyle H. Ungar, Romain Pirracchio, Eric Eaton, Lara G. Reichmann, Yannet Interian, et al. "Expert-augmented machine learning." Proceedings of the National Academy of Sciences 117, no. 9 (February 18, 2020): 4571–77. http://dx.doi.org/10.1073/pnas.1906831117.

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Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications.

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Journal articles on the topic 'Trust in machines'

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