“Do you still trust me?” Effects of Personality on Changes in Trust during an Experimental Task with a Human or Robot Partner

In the current study, we investigated the effects of dispositional variables on self-reported trust and suspicion perceptions of one’s partner in a maze-running task. Dispositional variables affect the extent to which people perceive and encode information in their environment. Prior research has shown that dispositional variables interact with situational variables in expressing behaviors. In order to test the effects of three dispositional variables (i.e., dispositional trust, dispositional distrust, and dispositional suspicion) on self-reported trust and suspicion perceptions towards a partner (a human or a Nao robot), we ran two discontinuous growth models. Overall, we found that participants’ trust towards their partner decreased when the partner engaged in untrustworthy behaviors as expected. In addition, changes in trust perceptions towards the partner were predicted by participants’ level of dispositional trust. These results have implications for studying the effects of dispositional variables on context-dependent trust perceptions within the trust process

The Effects of Age and Working Memory Demands on Automation-Induced Complacency

Complacency refers to a type of automation use expressed as insufficient monitoring and verification of automated functions. Previous studies have attempted to identify the age-related factors that influence complacency during interaction with automation. However, little is known about the role of age-related differences in working memory capacity and its connection to complacent behaviors. The current study examined whether working memory demand of an automated task and age-related differences in cognitive ability influence complacency. Working memory demand was manipulated in the task with two degrees of automation (i.e., information and decision). A younger and older age group was included to observe the effects of differences in working memory capacity on performance in a targeting task using an automated aid. The results of the study show that younger and older adults did not significantly differ in complacent behavior for information or decision automation. Also, individual differences in working memory capacity did not predict complacency in the automated task. However, these findings do not disprove the role of working memory in automation-induced complacency. Both age groups were more complacent with automation that had less working memory demand. Our findings suggest systems that utilize both higher and lower degrees of automation could limit overdependence. These results provide implications for the design of automated interfaces

Situational Awareness, Driver’s Trust in Automated Driving Systems and Secondary Task Performance

Driver assistance systems, also called automated driving systems, allow drivers to immerse themselves in non-driving-related tasks. Unfortunately, drivers may not trust the automated driving system, which prevents either handing over the driving task or fully focusing on the secondary task. We assert that enhancing situational awareness can increase a driver's trust in automation. Situational awareness should increase a driver's trust and lead to better secondary task performance. This study manipulated driversʼ situational awareness by providing them with different types of information: the control condition provided no information to the driver, the low condition provided a status update, while the high condition provided a status update and a suggested course of action. Data collected included measures of trust, trusting behavior, and task performance through surveys, eye-tracking, and heart rate data. Results show that situational awareness both promoted and moderated the impact of trust in the automated vehicle, leading to better secondary task performance. This result was evident in measures of self-reported trust and trusting behavior.This research was supported in part by the Automotive Research Center (ARC) at the University of Michigan, with funding from government contract Department of the Army W56HZV-14-2-0001 through the U. S. Army Tank Automotive Research, Development, and Engineering Center (TARDEC). The authors acknowledge and greatly appreciate the guidance of Victor Paul (TARDEC), Ben Haynes (TARDEC), and Jason Metcalfe (ARL) in helping design the study. The authors would also like to thank Quantum Signal, LLC, for providing its ANVEL software and invaluable development support.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148141/1/SA Trust - SAE- Public.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148141/4/Petersen et al. 2019.pdfDescription of Petersen et al. 2019.pdf : Final Publication Versio

Safety, Trust, and Ethics Considerations for Human-AI Teaming in Aerospace Control

Designing a safe, trusted, and ethical AI may be practically impossible; however, designing AI with safe, trusted, and ethical use in mind is possible and necessary in safety and mission-critical domains like aerospace. Safe, trusted, and ethical use of AI are often used interchangeably; however, a system can be safely used but not trusted or ethical, have a trusted use that is not safe or ethical, and have an ethical use that is not safe or trusted. This manuscript serves as a primer to illuminate the nuanced differences between these concepts, with a specific focus on applications of Human-AI teaming in aerospace system control, where humans may be in, on, or out-of-the-loop of decision-making

GERONTECNOLOGIA: O QUE MOSTRA A PRODUÇÃO CIENTÍFICA NOS ÚLTIMOS 20 ANOS?.

RESUMO Este estudo teve como objetivo explorar na literatura científica mundial, publicações sobre gerontecnologia e realizar uma análise destas publicações, buscando evidenciar a evolução temporal, bem como, os eixos temáticos. Foi realizado uma revisão cenciométrica por meio de uma análise da produção científica veiculada em periódicos indexados nas bases de dados SciELO, PubMed, Science Direct e Lilacs. Foi utilizado o descritor gerontecnologia e seu correspondente em inglês e espanhol. Foram identificados e coletados ano de publicação e eixo temático central. Os dados foram tabulados, organizados em planilhas do programa Microsoft Excel 2010 e empregada análise descritiva. Descartando-se as publicações duplicadas, obteve-se um total de 111 artigos científicos eleitos para este estudo. Com relação à evolução temporal foi evidenciado que a primeira publicação sobre o tema gerontologia datou de 1997 e, houve um aumento brusco em 1998, um declínio a zero em 1999, e nos anos seguintes, as publicações apresentam uma tendência a comportamento crescente constante. Já os eixos temáticos foram evidenciados em maior relevância: Mobilidade e Motricidade (16,2%); Cuidados Comunitários e Ambiente, ambos com (15,3%); Capacidades Sensoriais e Cognitivas (10,8%) e Design e Ergonomia (9,9%). Conclui-se que existe uma tendência a implantação da gerontecnologia nos estudos e são distintos os eixos abordados por esta temática, além disso a tecnologia é uma nova abordagem de promover saúde e qualidade de vida aos idosos no contexto da interdisciplinaridade. Palavras-chave: Envelhecimento; Tecnologia; Promoção da Saúde. GERONTECNOLOGY: WHAT SHOWS A SCIENTIFIC PRODUCTION IN THE LAST 20 YEARS? ABSTRACT This study aimed to explore in the scientific literature worldwide, publications on gerontecnologia and to carry out an analysis of these publications, seeking to evidence the temporal evolution, as well as, the thematic axes. A centimeter revision was carried out through an analysis of the scientific production published in journals indexed in the SciELO, PubMed, Science Direct and Lilacs databases. The descriptor gerontecnologia and its correspondent in English and Spanish were used. The year of publication and central theme were identified and collected. The data were tabulated, organized into spreadsheets of the program Microsoft Excel 2010 and employed descriptive analysis. Discarding duplicate publications, we obtained a total of 111 scientific articles chosen for this study. Regarding historical evolution, it was evidenced that the first publication on gerontology dates back to 1997, and there was an abrupt increase in 1998, a decline to zero in 1999, and in the following years, publications show a trend towards steadily increasing behavior. The thematic axes were evidenced in greater relevance: Mobility and Mobility (16.2%); Community Care and Environment, both with (15.3%); Sensory and Cognitive Capabilities (10.8%) and Design and Ergonomics (9.9%). It is concluded that there is a trend towards the implantation of gerontecnologia in the studies and the axes addressed by this theme are distinct, in addition technology is a new approach to promote health and quality of life for the elderly in the context of interdisciplinarity. KEY WORDS: Aging; Technology; Health promotion

Trust in an autonomously driven simulator and vehicle performing maneuvers at a T-junction with and without other vehicles

Autonomous vehicle (AV) technology is developing rapidly. Level 3 automation assumes the user might need to respond to requests to retake control. Levels 4 (high automation) and 5 (full automation) do not require human monitoring of the driving task or systems [1]: the AV handles driving functions and makes decisions based on continuously updated information. A gradual switch in the role of the human within the vehicle from active controller to passive passenger comes with uncertainty in terms of trust, which will likely be a key barrier to acceptability, adoption and continued use [2]. Few studies have investigated trust in AVs and these have tended to use driving simulators with Level 3 automation [3, 4]. The current study used both a driving simulator and autonomous road vehicle. Both were operating at Level 3 autonomy although did not require intervention from the user; much like Level 4 systems. Forty-six participants completed road circuits (UK-based) with both platforms. Trust was measured immediately after different types of turns at a priority T-junction, increasing in complexity: e.g., driving left or right out of a T-junction; turning right into a T-junction; presence of oncoming/crossing vehicles. Trust was high across platforms: higher in the simulator for some events and higher in the road AV for others. Generally, and often irrespective of platform, trust was higher for turns involving an oncoming/crossing vehicle(s) than without traffic, possibly because the turn felt more controlled as the simulator and road AVs always yielded, resulting in a delayed maneuver. We also found multiple positive relationships between trust in automation and technology, and trust ratings for most T-junction turn events across platforms. The assessment of trust was successful and the novel findings are important to those designing, developing and testing AVs with users in mind. Undertaking a trial of this scale is complex and caution should be exercised about over-generalizing the findings

UX in AI: Trust in Algorithm-based Investment Decisions

This Thesis looks at investors’ loss tolerance with portfolios managed by a human advisor compared to an algorithm with different degrees of humanization. The main goal is to explore differences between these groups (Humanized Algorithm, Dehumanized Algorithm, Humanized Human and Dehumanized Humans) and a potential diverging effect of humanizing. The Thesis is based on prior research (Hodge et al., 2018) but incorporates new aspects such as additional variables (demographics, prior experiences) and a comparison between users and non-users of automated-investment products. The core of this research is an experiment simulating an investment portfolio over time with four different portfolio managers. Subjects were asked to decide if they want to hold or sell a declining portfolio at five points in time to measure their loss tolerance. A cox regression model shows that portfolios managed by the Humanized Human had the highest loss tolerance. Humanizing leads to higher loss tolerance for the human advisor but to lower loss tolerance for algorithmic advisors within the non-user group. Keywords: Künstliche Intelligenz; Artificial Intelligence; Behavioral Finance; Behavioral Economics; Human-Computer-Interaction; User Experience; Investmententscheidungen; Nutzervertrauen