Human Trust Research Articles

A System Dynamics Model for Human Trust in Automation under Speed and Accuracy Requirements

Research shows that human trust in automation is a key predictor of human reliance on the automation. Several models have been proposed to capture the interplay between trust and reliance and their combined impacts on task performance. Whereas some models assume that trust is affected by automation reliability, others assume that trust is affected by automation speed. In fact, both speed and reliability can be crucial for mission performance, therefore, these models do not represent the interrelationships among automation speed, automation reliability, human decision making, and subsequent effects on mission performance. To address this gap, we propose a system dynamics model which incorporates both the speed and reliability of automation and their combined effects on trust. Our model explicitly represents the speed-accuracy compromise adopted by the subjects to weigh the perceived relative importance of these aspects while evaluating the reliance decision. The model is calibrated and evaluated using data collected from a human experiment in which 33 subjects interacted with an automated aid for swarm supervision in a foraging mission. The simulation results show that the model can closely replicate and predict the experimental data in terms of the reliance rate and the number of targets collected. Model limitations and further efforts for model extension are discussed.

Generational differences in trust in digital assistants

Human trust in automation has been studied extensively within safety critical domains (military, aviation, process control, etc.) because harmful consequences are associated with the improper calibration of trust in automated systems in these domains (Parasuraman & Riley, 1997). As such, researchers have worked to identify important factors which help humans build trust in such systems (Hoff & Bashir, 2015). With the explosion of AI in consumer technologies, it is becoming equally critical to understand how humans interact with everyday devices. This study investigated how factors that have been identified to impact trust in automation in safety critical domains influence the trust and use of popular digital assistants (Siri, Cortana, Bixby or Google Now). We conducted an online survey with 278 regular users of digital assistants across three generations (GenX, GenY, and GenZ). The results demonstrate that, even after controlling for dispositional factors (i.e., individual characteristics such as age, culture, gender), GenZ exhibited higher trust in digital assistants than GenX. More interestingly, linear regression analyses revealed a different set of predictors of trust for each generation. Results from this survey have implications for the design of digital assistants.

Practical Guidance for Evaluating Calibrated Trust

As automation, autonomy, and AI become more prevalent, human factors engineers are called to evaluate whether users trust that automation. We argue that the true question is whether users trust the automation appropriately. In other words, do they trust it as much as it deserves to be trusted? When automation performance decreases, are users aware so they decrease their momentary trust, and more importantly, their reliance on the automation? There are few metrics that focus specifically on calibrated trust, and the trust literature can be daunting for human factors professionals who are not experts in trust. We offer two aids to human factors practitioners tasked with evaluating trust. The first is an easy-to-use calibrated trust framework that simplifies the aspects of trust into Belief, Understanding, Intent, and Reliance. The second is the introduction of Calibration Points, a way to classify situations in which the automation excels or situations in which the automation is degraded. By identifying these Calibration Points, human factors practitioners can evaluate whether human trust is aligned with automation performance. This approach allows the practitioner to leverage the rich set of evaluation techniques that have been developed to evaluate trust.

Detecting Human Trust Calibration in Automation: A Deep Learning Approach

Detecting Human Trust Calibration in Automation: A Deep Learning Approach

Tawakal Dalam Perspektif Islam

Man was created by God as a perfect being because he was blessed with reason so that he can distinguish between good and bad, haq and inner. There are two things that cannot be released in human life, namely endeavor and trust. Efforts are to try and do, not be silent, also not fatalistic. His conviction is quite strong and stable, while trust is an attitude of a servant who is the result of his unanimous belief in God, because he believes that only God created everything, his knowledge is extensive, he is the one who governs and controls the universe. It is this belief that drives him to surrender all his problems to God. His heart is calm and peaceful and there is no suspicion, because Allah is Omniscient and Wise, and there are still many versions of scholars in interpreting tawakal. There are 4 things that must be known in trust, namely: the meaning of trust, the degree of trust, trust in benefits, and characteristics of trust. By knowing the four points of trust, a person can strengthen himself to put his trust in God.

Trust Mediating Reliability-Reliance Relationship in Supervisory Control of Human-Swarm Interactions.

This work aims to further test the theory that trust mediates the interdependency between automation reliability and the rate of human reliance on automation. Human trust in automation has been the focus of many research studies. Theoretically, trust has been proposed to impact human reliance on automation by mediating the relationship between automation reliability and the rate of human reliance. Experimentally, however, the results are contradicting as some confirm the mediating role of trust, whereas others deny it. Hence, it is important to experimentally reinvestigate this role of trust and understand how the results should be interpreted in the light of existing theory. Thirty-two subjects supervised a swarm of unmanned aerial vehicles (UAVs) in foraging missions in which the swarm provided recommendations on whether or not to collect potential targets, based on the information sensed by the UAVs. By manipulating the reliability of the recommendations, we observed changes in participants' trust and their behavioral responses. A within-subject mediation analysis revealed a significant mediation role of trust in the relationship between swarm reliability and reliance rate. High swarm reliability increased the rate of correct acceptances, but decreased the rate of correct rejections. No significant effect of reliability was found on response time. Trust is not a mere by-product of the interaction; it possesses a predictive power to estimate the level of reliance on automation. The mediation role of trust confirms the significance of trust calibration in determining the appropriate level of reliance on swarm automation.

Understanding the Influences of Past Experience on Trust in Human-agent Teamwork

People use the knowledge acquired from past experiences in assessing the trustworthiness of a trustee. In a time where the agents are being increasingly accepted as partners in collaborative efforts and activities, it is critical to understand all aspects of human trust development in agent partners. For human-agent virtual ad hoc teams to be effective, humans must be able to trust their agent counterparts. To earn the humans’ trust, agents need to quickly develop an understanding of the expectation of human team members and adapt accordingly. This study empirically investigates the impact of past experience on human trust in and reliance on agent teammates. To do so, we developed a team coordination game, the Game of Trust (GoT), in which two players repeatedly cooperate to complete team tasks without prior assignment of subtasks. The effects of past experience on human trust are evaluated by performing an extensive set of controlled experiments with participants recruited from Amazon Mechanical Turk, a crowdsourcing marketplace. We collect both teamwork performance data as well as surveys to gauge participants’ trust in their agent teammates. The results show that positive (negative) past experience increases (decreases) human trust in agent teammates; lack of past experience leads to higher trust levels compared to positive past experience; positive (negative) past experience facilitates (hinders) reliance on agent teammates; the relationship between trust in and reliance on agent teammates is not always correlated. These findings provide clear and significant evidence of the influence of key factors on human trust in virtual agent teammates and enhance our understanding of the changes in human trust in peer-level agent teammates with respect to past experience.

Multi-task trust transfer for human–robot interaction

Trust is essential in shaping human interactions with one another and with robots. In this article we investigate how human trust in robot capabilities transfers across multiple tasks. We present a human-subject study of two distinct task domains: a Fetch robot performing household tasks and a virtual reality simulation of an autonomous vehicle performing driving and parking maneuvers. The findings expand our understanding of trust and provide new predictive models of trust evolution and transfer via latent task representations: a rational Bayes model, a data-driven neural network model, and a hybrid model that combines the two. Experiments show that the proposed models outperform prevailing models when predicting trust over unseen tasks and users. These results suggest that (i) task-dependent functional trust models capture human trust in robot capabilities more accurately and (ii) trust transfer across tasks can be inferred to a good degree. The latter enables trust-mediated robot decision-making for fluent human–robot interaction in multi-task settings.

Making IoT Worthy of Human Trust

The Public Key Infrastructure (PKI) is the foundation which enables secure and trusted transactions across the Internet. PKI is subject to both continuous attacks and regular improvements; for example, advances in cryptography have led to rejections of previously trusted algorithms (i.e., SHA1, MD5). Yet there have also been organizational failures and malicious acts by trusted parties. In this work, we focus on the sociotechnical components of the current X.509 PKI with the goals of better understanding its vulnerabilities, and ideally informing the implementation of future PKIs. We begin with a taxonomy of chronic, catastrophic, high impact, or frequent PKI failures. This categorization was informed by a survey of non-expert perceptions of PKI and an interdisciplinary workshop addressing the future of security in the Internet of Things. To evaluate the failure modes, we conducted qualitative interviews with policy scholars and experts in applied cryptography. We summarize the results of the survey and workshop, and detail the expert interviews. Our findings indicate that there are significant failure types which neither the technical nor policy community are deeply engaging. The underlying assumptions about rate and severity of failure differ between these communities. Yet there is a common awareness of the vulnerabilities of the end users: the people who are required to trust PKI to interact and engage with the Internet. We identify an urgency in mitigating such critical issues, because of the increasing adoption of cyberphysical systems and the Internet of Things (IoT). We concluded that there is a need for integrated organizational, policy, and technical coordination to address the chronic and potentially catastrophic risks. We introduce possible economic and regulatory solutions, and highlight the key takeaways which pave our future research directions.

Reasoning about Cognitive Trust in Stochastic Multiagent Systems

We consider the setting of stochastic multiagent systems modelled as stochastic multiplayer games and formulate an automated verification framework for quantifying and reasoning about agents’ trust. To capture human trust, we work with a cognitive notion of trust defined as a subjective evaluation that agent A makes about agent B ’s ability to complete a task, which in turn may lead to a decision by A to rely on B . We propose a probabilistic rational temporal logic PRTL*, which extends the probabilistic computation tree logic PCTL* with reasoning about mental attitudes (beliefs, goals, and intentions) and includes novel operators that can express concepts of social trust such as competence, disposition, and dependence. The logic can express, for example, that “agent A will eventually trust agent B with probability at least p that B will behave in a way that ensures the successful completion of a given task.” We study the complexity of the automated verification problem and, while the general problem is undecidable, we identify restrictions on the logic and the system that result in decidable, or even tractable, subproblems.

Neural Correlates of Variations in Human Trust in Human-like Machines during Non-reciprocal Interactions

As intelligent machines have become widespread in various applications, it has become increasingly important to operate them efficiently. Monitoring human operators’ trust is required for productive interactions between humans and machines. However, neurocognitive understanding of human trust in machines is limited. In this study, we analysed human behaviours and electroencephalograms (EEGs) obtained during non-reciprocal human-machine interactions. Human subjects supervised their partner agents by monitoring and intervening in the agents’ actions in this non-reciprocal interaction, which reflected practical uses of autonomous or smart systems. Furthermore, we diversified the agents with external and internal human-like factors to understand the influence of anthropomorphism of machine agents. Agents’ internal human-likenesses were manifested in the way they conducted a task and affected subjects’ trust levels. From EEG analysis, we could define brain responses correlated with increase and decrease of trust. The effects of trust variations on brain responses were more pronounced with agents who were externally closer to humans and who elicited greater trust from the subjects. This research provides a theoretical basis for modelling human neural activities indicate trust in partner machines and can thereby contribute to the design of machines to promote efficient interactions with humans.

Association of Polymorphism of Arginine-Vasopressin Receptor 1A (AVPR1a) Gene With Trust and Reciprocity.

Oxytocin (OXT) is known to play an important role in trust, whereas the involvement of other peptide hormones has not been evaluated. In this study, we focused on microsatellite polymorphisms in the intron of the arginine-vasopressin receptor 1a (AVPR1a) gene and examined whether the association between the repeat lengths in the intron of AVPR1a is associated with trust and reciprocity in humans. Four-hundred and thirty-three participants played the trust game, answered the attitudinal trust question, and their buccal cells were collected. Results showed that men with a short form of AVPR1a tend to send more money to the opponent, even if there is a possibility of being betrayed by the opponent. Additionally, people with a short form of AVPR1a tended to return money to the opponent who trusts them. However, attitudinal trust was not associated with AVPR1a. These results indicate that arginine-vasopressin receptor 1a plays an important role in trust and reciprocal behaviors.

Emotional expressions facilitate human–human trust when using automation in high-risk situations

ABSTRACTThis study investigated the utility of emotional expression for human decision aids, when human aids conflict with an automated decision support system (DSS). The increasing presence of automation in society has resulted in critical, and often life threatening, situations when information from human and automated sources disagree. It has been known that reliance on human aids decrease during high-risk situations, while reliance on automated aids increase. However, it is also possible that human decision aids gain credibility from users when they embody the charismatic and emotionally expressive gesticulations seen in successful organizational leaders. The present study tested how a human agent's expressiveness when providing information would influence participants' behavioral reliance. Using the program Convoy Leader, participants (n=56) engaged in three decision-making scenarios where risk was manipulated as a within-subject factor and emotional expression as a between-subject factor. Emotional susceptibility, perceived risk, and trust for human as well as automated aids were measured. Overall trust was higher for the automated tool than human decision aid, and that pattern was amplified in conditions without an emotionally expressive human aid. Reliance was greater for emotionally expressive human aids, than stoic human aids, particularly during high risk conditions. The findings suggest that emotional expression of a human aid significantly impacts both reliance and trust of a decision aid, especially at higher risk levels. Emotionally expressive human agents should be utilized in decision conflicts where the automated system has certainly failed.

Variation in the μ-Opioid Receptor Gene (OPRM1) Does Not Moderate Social-Rejection Sensitivity in Humans

Given previous findings from animal studies and small-scale studies in humans, variation in the μ-opioid receptor gene (OPRM1) has been proposed as a strong biological candidate for moderating sensitivity to social rejection. Using a substantially larger sample (N = 490) than previous studies, a prospective genotyping strategy, and preregistered analysis plans, we tested the hypotheses that OPRM1 variation measured by the functional A118G polymorphism (rs1799971) moderates (a) dispositional sensitivity to rejection and feelings of distress following social exclusion and (b) decision making involving social cognition. In three experimental tasks commonly used to assess altruism, reciprocity, and trust in humans, we found no evidence in favor of the hypotheses; nine main tests were preregistered, and all of them yielded small and statistically insignificant estimates. In secondary analyses, we used Bayesian inference and estimation to quantify support for our findings. Taken together, our results strongly suggest that the link between OPRM1 A118G variation and social-rejection sensitivity is weaker than previously thought.

Technological Populism and Its Archetypes: Blockchain and Cryptocurrencies

Blockchain technology claims to disrupt the existing financial system, the way of doing business, and to empower ordinary citizens against elitist economy through decentralization of decision-making process. In the political arena, the disruptive ideology branded as ‘populism’ challenges the neo-liberal establishment. By appealing to peoples’ fears, frustrations, and dissatisfaction with the political elites, exploiting distrust in the so-called establishment, populism claims to deliver more power to the people. In this article, we draw a parallel between core foundations of political populism and those of blockchain and propose a theory of technological populism. Technological populism as reflected by blockchain platforms exploits the rhetoric of empowering the disenfranchised through decentralized decision-making process, enabling anonymous of transactions, dehumanizing trust (trust in computation rather than trust in humans and institutions) as well as breaking the monopoly in the financial system and money supply. The rhetoric of empowering the disenfranchised against financial elites is not only propaganda, but also a method of accumulating wealth for technocratic elites. Ultimately, the blockchain and cryptocurrency world has perfected what political populists have pioneered — unrealistic promises, turning the citizen against “the elites” only so long as they are not the elites in charge.

A Study on Factors influencing ERM & Trust among employees in Manufacturing Companies

This research investigates the effect of Factors influencing ERM & Trust among employees in Machine tools Manufacturing Companies. A good relationship is necessary for the smooth running of any business. A good communication between an employer and its employees is imperative for building a positive work place culture. Gratitude & Appreciations is very important. ERM main components are (Human Resources Practices, Communication, Trust, Shared Goals and Values, and Leadership Styles). Employee relationship management has emerged as one of the important tool for motivating employees. In this study statistical tools like weighted average & Chi-Square test are used to find out the best factor & Association.

Humans Trust Central Vision More Than Peripheral Vision Even in the Dark

SummaryTwo types of photoreceptors in the human retina support vision across a wide range of luminances: cones are active under bright daylight illumination (photopic viewing) and rods under dim illumination at night (scotopic viewing). These photoreceptors are distributed inhomogeneously across the retina [1]: cone-receptor density peaks at the center of the visual field (i.e., the fovea) and declines toward the periphery, allowing for high-acuity vision at the fovea in daylight. Rod receptors are absent from the fovea, leading to a functional foveal scotoma in night vision. In order to make optimal perceptual decisions, the visual system requires knowledge about its own properties and the relative reliability of signals arriving from different parts of the visual field [2]. Since cone and rod signals converge on the same pathways [3], and their cortical processing is similar except for the foveal scotoma [4], it is unclear if humans can take into account the differences between scotopic and photopic vision when making perceptual decisions. Here, we show that the scotopic foveal scotoma is filled in with information from the immediate surround and that humans trust this inferred information more than veridical information from the periphery of the visual field. We observed a similar preference under daylight illumination, indicating that humans have a default preference for information from the fovea even if this information is not veridical, like in night vision. This suggests that filling-in precedes the estimation of confidence, thereby shielding awareness from the foveal scotoma with respect to its contents and its properties.

The New Statistics for Better Science: Ask How Much, How Uncertain, and What Else Is Known

The “New Statistics” emphasizes effect sizes, confidence intervals, meta-analysis, and the use of Open Science practices. We present three specific ways in which a New Statistics approach can help improve scientific practice: by reducing overconfidence in small samples, by reducing confirmation bias, and by fostering more cautious judgments of consistency. We illustrate these points through consideration of the literature on oxytocin and human trust, a research area that typifies some of the endemic problems that arise with poor statistical practice.

Models of Trust in Human Control of Swarms With Varied Levels of Autonomy

In this paper, we study human trust and its computational models in supervisory control of swarm robots with varied levels of autonomy (LOA) in a target foraging task. We implement three LOAs: manual, mixed-initiative (MI), and fully autonomous LOA. While the swarm in the MI LOA is controlled by a human operator and an autonomous search algorithm collaboratively, the swarms in the manual and autonomous LOAs are fully directed by the human and the search algorithm, respectively. From user studies, we find that humans tend to make their decisions based on physical characteristics of the swarm rather than its performance since the task performance of swarms is not clearly perceivable by humans. Based on the analysis, we formulate trust as a Markov decision process whose state space includes the factors affecting trust. We develop variations of the trust model for different LOAs. We employ an inverse reinforcement learning algorithm to learn behaviors of the operator from demonstrations where the learned behaviors are used to predict human trust. Compared to an existing model, our models reduce the prediction error by at most 39.6%, 36.5%, and 28.8% in the manual, MI, and auto-LOA, respectively.

Logistics Innovation and Social Sustainability: How to Prevent an Artificial Divide in Human–Computer Interaction

Human–computer interaction (HCI) is a cornerstone for the success of technical innovation in the logistics and supply chain sector. As a major part of social sustainability, this interaction is changing as artificial intelligence applications (Internet of Things, autonomous transport, Physical Internet) are implemented, leading to larger machine autonomy, and hence the transition from a primary executive to a supervisory role of human operators. A fundamental question concerns the level of control transferred to machines, such as autonomous vehicles and automatic materials handling devices. Problems include a lack of human trust toward automatic decision making or an inclination to override the system in case automated decisions are misperceived. This paper outlines a theoretical framework, describing different levels of acceptance and trust as a key HCI element of technology innovation, and points to the possible danger of an artificial divide at both the individual and firm level. Based upon the findings of four benchmark cases, a classification of the roles of human employees in adopting innovations is developed. Measures at operational, tactical, and strategic level are discussed to improve HCI, more in particular the capacity of individuals and firms to apply state‐of‐the‐art techniques and to prevent an artificial divide, thereby increasing social sustainability.