Getting Real: A Naturalistic Methodology for Using Smartphones to Collect Mediated Communications

This paper contributes an intentionally naturalistic methodology using smartphone logging technology to study communications in the wild. Smartphone logging can provide tremendous access to communications data from real environments. However, researchers must consider how it is employed to preserve naturalistic behaviors. Nine considerations are presented to this end. We also provide a description of a naturalistic logging approach that has been applied successfully to collecting mediated communications from iPhones. The methodology was designed to intentionally decrease reactivity and resulted in data that were more accurate than self-reports. Example analyses are also provided to show how data collected can be analyzed to establish empirical patterns and identify user differences. Smartphone logging technologies offer flexible capabilities to enhance access to real communications data, but methodologies employing these techniques must be designed appropriately to avoid provoking naturally occurring behaviors. Functionally, this methodology can be applied to establish empirical patterns and test specific hypotheses within the field of HCI research. Topically, this methodology can be applied to domains interested in understanding mediated communications such as mobile content and systems design, teamwork, and social networks

Trusting the Moral Judgments of a Robot: Perceived Moral Competence and Humanlikeness of a GPT-3 Enabled AI

Advancements in computing power and foundational modeling have enabled artificial intelligence (AI) to respond to moral queries with surprising accuracy. This raises the question of whether we trust AI to influence human moral decision-making, so far, a uniquely human activity. We explored how a machine agent trained to respond to moral queries (Delphi, Jiang et al., 2021) is perceived by human questioners. Participants were tasked with querying the agent with the goal of figuring out whether the agent, presented as a humanlike robot or a web client, was morally competent and could be trusted. Participants rated the moral competence and perceived morality of both agents as high yet found it lacking because it could not provide justifications for its moral judgments. While both agents were also rated highly on trustworthiness, participants had little intention to rely on such an agent in the future. This work presents an important first evaluation of a morally competent algorithm integrated with a human-like platform that could advance the development of moral robot advisors

Group trust dynamics during a risky driving experience in a Tesla Model X

The growing concern about the risk and safety of autonomous vehicles (AVs) has made it vital to understand driver trust and behavior when operating AVs. While research has uncovered human factors and design issues based on individual driver performance, there remains a lack of insight into how trust in automation evolves in groups of people who face risk and uncertainty while traveling in AVs. To this end, we conducted a naturalistic experiment with groups of participants who were encouraged to engage in conversation while riding a Tesla Model X on campus roads. Our methodology was uniquely suited to uncover these issues through naturalistic interaction by groups in the face of a risky driving context. Conversations were analyzed, revealing several themes pertaining to trust in automation: (1) collective risk perception, (2) experimenting with automation, (3) group sense-making, (4) human-automation interaction issues, and (5) benefits of automation. Our findings highlight the untested and experimental nature of AVs and confirm serious concerns about the safety and readiness of this technology for on-road use. The process of determining appropriate trust and reliance in AVs will therefore be essential for drivers and passengers to ensure the safe use of this experimental and continuously changing technology. Revealing insights into social group–vehicle interaction, our results speak to the potential dangers and ethical challenges with AVs as well as provide theoretical insights on group trust processes with advanced technology

Characterizing web use on smartphones

The current paper establishes empirical patterns associated with mobile internet use on smartphones and explores user differences in these behaviors. We apply a naturalistic and longitudinal logs-based approach to collect real usage data from 24 iPhone users in the wild. These data are used to describe smartphone usage and analyze revisitation patterns of web browsers, native applications, and physical locations where phones are used. Among our findings are that web page revisitation through browsers occurred very infrequently (approximately 25 % of URLs are revisited by each user), bookmarks were used sparingly, physical traversing patterns mirrored virtual (internet) traversing patterns and users systematically differed in their web use. We characterize these differences and suggest ways to support users with enhanced design of smartphone technologies and content. Author Keyword

Optimizing Data Processing and Management Decisions During Isr Through Innovative Training Regimens

Effective intelligence, surveillance, and reconnaissance (ISR) relies heavily on both technological and human analytical capabilities. Intelligence analysts must be able to detect, interpret, process, and perform other critical tasks to turn data into meaningful information for decision-makers. The ability to aggregate massive data sets into operationally relevant information is challenging due to issues such as information overload, team coordination, time constraints, tunnel vision, and limited or vague guidance. This report describes research and development efforts to enhance training for geospatial intelligence analysts. Initial results from cognitive task analyses with these analysts along with associated technology development are discussed

Designing Man’s New Best Friend: Enhancing Human-Robot Dog Interaction through Dog-Like Framing and Appearance

To understand how to improve interactions with dog-like robots, we evaluated the importance of “dog-like” framing and physical appearance on interaction, hypothesizing multiple interactive benefits of each. We assessed whether framing Aibo as a puppy (i.e., in need of development) versus simply a robot would result in more positive responses and interactions. We also predicted that adding fur to Aibo would make it appear more dog-like, likable, and interactive. Twenty-nine participants engaged with Aibo in a 2 × 2 (framing × appearance) design by issuing commands to the robot. Aibo and participant behaviors were monitored per second, and evaluated via an analysis of commands issued, an analysis of command blocks (i.e., chains of commands), and using a T-pattern analysis of participant behavior. Participants were more likely to issue the “Come Here” command than other types of commands. When framed as a puppy, participants used Aibo’s dog name more often, praised it more, and exhibited more unique, interactive, and complex behavior with Aibo. Participants exhibited the most smiling and laughing behaviors with Aibo framed as a puppy without fur. Across conditions, after interacting with Aibo, participants felt Aibo was more trustworthy, intelligent, warm, and connected than at their initial meeting. This study shows the benefits of introducing a socially robotic agent with a particular frame and importance on realism (i.e., introducing the robot dog as a puppy) for more interactive engagement