God-like robots: the semantic overlap between representation of divine and artificial entities

The roles of cognitive dissonance and normative reasoning in attributions of minds to robots

As a wide variety of intelligent technologies become part of everyday life, researchers have explored how people conceptualize agents that in some ways act and think like living things but are clearly machines. Much of this work draws upon the idea that people readily default to generalizing human-like properties to such agents, and only pare back on these generalizations with added thought. However, recent findings have also documented that people are sometimes initially hesitant to attribute minds to a machine but are more willing to do so with additional thought. In the current experiments, we hypothesized that these attribution-increasing reconsiderations could be spurred by situation-induced cognitive dissonance. In two experiments, participants completed a belief activation exercise designed to induce cognitive dissonance (writing arguments for or against prominent beliefs), viewed a video of an ambiguously intentional robot, and completed measures of cognitive dissonance. In both experiments, cognitive dissonance was associated with increased attributions of mind to the robot. Our findings provide evidence that people sometimes increase their attributions of minds when experiencing cognitive conflict, but also that activation of change-inducing concepts may impact attributions of a mind without producing conscious cognitive conflict in participants.

The positive-negative-competence (PNC) model of psychological responses to representations of robots

Robots are becoming an increasingly prominent part of society. Despite their growing importance, there exists no overarching model that synthesizes people's psychological reactions to robots and identifies what factors shape them. To address this, we created a taxonomy of affective, cognitive and behavioural processes in response to a comprehensive stimulus sample depicting robots from 28 domains of human activity (for example, education, hospitality and industry) and examined its individual difference predictors. Across seven studies that tested 9,274 UK and US participants recruited via online panels, we used a data-driven approach combining qualitative and quantitative techniques to develop the positive-negative-competence model, which categorizes all psychological processes in response to the stimulus sample into three dimensions: positive, negative and competence-related. We also established the main individual difference predictors of these dimensions and examined the mechanisms for each predictor. Overall, this research provides an in-depth understanding of psychological functioning regarding representations of robots.

Different models of anthropomorphism across cultures and ontological limits in current frameworks the integrative framework of anthropomorphism

Anthropomorphism describes the tendency to ascribe human characteristics to nonhuman agents. Due to the increased interest in social robotics, anthropomorphism has become a core concept of human-robot interaction (HRI) studies. However, the wide use of this concept resulted in an interchangeability of its definition. In the present study, we propose an integrative framework of anthropomorphism (IFA) encompassing three levels: cultural, individual general tendencies, and direct attributions of human-like characteristics to robots. We also acknowledge the Western bias of the state-of-the-art view of anthropomorphism and develop a cross-cultural approach. In two studies, participants from various cultures completed tasks and questionnaires assessing their animism beliefs, individual tendencies to endow robots with mental properties, spirit, and consider them as more or less human. We also evaluated their attributions of mental anthropomorphic characteristics towards robots (i.e., cognition, emotion, intention). Our results demonstrate, in both experiments, that a three-level model (as hypothesized in the IFA) reliably explains the collected data. We found an overall influence of animism (cultural level) on the two lower levels, and an influence of the individual tendencies to mentalize, spiritualize and humanize (individual level) on the attribution of cognition, emotion and intention. In addition, in Experiment 2, the analyses show a more anthropocentric view of the mind for Western than East-Asian participants. As such, Western perception of robots depends more on humanization while East-Asian on mentalization. We further discuss these results in relation to the anthropomorphism literature and argue for the use of integrative cross-cultural model in HRI research.

Applications of Machine Learning in Knowledge Management System: A Comprehensive Review

As new generations of technology appear, legacy knowledge management solutions and applications become increasingly out of date, necessitating a paradigm shift. Machine learning presents an opportunity by foregoing rule-based knowledge intensive systems inundating the marketplace. An extensive review was made on the literature pertaining to machine learning which common machine learning algorithms were identified. This study has analysed more than 200 papers extracted from Scopus and IEEE databases. Searches ranged with the bulk of the articles from 2018 to 2021, while some articles ranged from 1959 to 2017. The research gap focusses on implementing machine learning algorithm to knowledge management systems, specifically knowledge management attributes. By investigating and reviewing each algorithm extensively, the usability of each algorithm is identified, with its advantages and disadvantages. From there onwards, these algorithms were mapped for what area of knowledge management it may be beneficial. Based on the findings, it is evidently seen how these algorithms are applicable in knowledge management and how it can enhance knowledge management system further. Based on the findings, the paper aims to bridge the gap between the literature in knowledge management and machine learning. A knowledge management–machine learning framework is conceived based on the review done on each algorithm earlier and to bridge the gap between the two literatures. The framework highlights how machine learning algorithm can play a part in different areas of knowledge management. From the framework, it provides practitioners how and where to implement machine learning in knowledge management.

Cognitive Impact of Anthropomorphized Robot Gaze

Attentional control does not have fix functioning and can be strongly impacted by the presence of other human beings or humanoid robots. In two studies, this phenomenon was investigated while focusing exclusively on robot gaze as a potential determinant of attentional control along with the role of participants’ anthropomorphic inferences toward the robot. In study 1, we expected and found higher interference in trials including a direct robot gaze compared to an averted gaze on a task measuring attentional control (Eriksen flanker task). Participants’ anthropomorphic inferences about the social robot mediated this interference. In study 2, we found that averted gazes congruent with the correct answer (same task as study 1) facilitated performance. Again, this effect was mediated by anthropomorphic inferences. These two studies show the importance of anthropomorphic robotic gaze on human cognitive processing, especially attentional control, and also open new avenues of research in social robotics.

The AI doctor will see you now: assessing the framing of AI in news coverage

One of the sectors for which Artificial Intelligence applications have been considered as exceptionally promising is the healthcare sector. As a public-facing sector, the introduction of AI applications has been subject to extended news coverage. This article conducts a quantitative and qualitative data analysis of English news media articles covering AI systems that allow the automation of tasks that so far needed to be done by a medical expert such as a doctor or a nurse thereby redistributing their agency. We investigated in this article one particular framing of AI systems and their agency: the framing that positions AI systems as (1a) replacing and (1b) outperforming the human medical expert, and in which (2) AI systems are personified and/or addressed as a person. The analysis of our data set consisting of 365 articles written between the years 1980 and 2019 will show that there is a tendency to present AI systems as outperforming human expertise. These findings are important given the central role of news coverage in explaining AI and given the fact that the popular frame of ‘outperforming’ might place AI systems above critique and concern including the Hippocratic oath. Our data also showed that the addressing of an AI system as a person is a trend that has been advanced only recently and is a new development in the public discourse about AI.

Implicit Attitudes Towards Robots Predict Explicit Attitudes, Semantic Distance Between Robots and Humans, Anthropomorphism, and Prosocial Behavior: From Attitudes to Human–Robot Interaction

How people behave towards others relies, to a large extent, on the prior attitudes that they hold towards them. In Human–Robot Interactions, individual attitudes towards robots have mostly been investigated via explicit reports that can be biased by various conscious processes. In the present study, we introduce an implicit measure of attitudes towards robots. The task utilizes the measure of semantic priming to evaluate whether participants consider humans and robots as similar or different. Our results demonstrate a link between implicit semantic distance between humans and robots and explicit attitudes towards robots, explicit semantic distance between robots and humans, perceived robot anthropomorphism, and pro/anti-social behavior towards a robot in a real life, interactive scenario. Specifically, attenuated semantic distance between humans and robots in the implicit task predicted more positive explicit attitudes towards robots, attenuated explicit semantic distance between humans and robots, attribution of an anthropomorphic characteristic, and consequently a future prosocial behavior towards a robot. Crucially, the implicit measure of attitudes towards robots (implicit semantic distance) was a better predictor of a future behavior towards the robot than explicit measure of attitudes towards robots (self-reported attitudes). Cumulatively, the current results emphasize a new approach to measure implicit attitudes towards robots, and offer a starting point for further investigations of implicit processing of robots.