•This study first outlines the rise of emotional AI technologies in society and their important features.•Then it reviews and critiques the two commonly used methods namely Technological Acceptance ...Model and Moral Foundation Theory when applied to study attitudes toward emotional AI technologies.•We propose to bring these two theories together under the analytical Three-Pronged approach: Contexts, Variables, and Statistical Models.
Emotional artificial intelligence (AI) is a narrow, weak form of an AI system that reads, classifies, and interacts with human emotions. This form of smart technology has become an integral layer of our digital and physical infrastructures and will radically transform how we live, learn, and work. Not only will emotional AI provide numerous benefits (i.e., increased attention and awareness, optimized productivity, stress management, etc.), but in sensing and interacting with our intimate emotions, it seeks to surreptitiously modify human behaviors. This study proposes to bring together the Technological Acceptance Model (TAM) and the Moral Foundation Theory to study determinants of emotional AI's acceptance under the analytical framework of the Three-pronged Approach (Contexts, Variables, and Statistical models). We argue that to quantitatively study the acceptance of new technologies, it is necessary to leverage two intuitions. The first is the degree of acceptance increases with how users of smart technology perceive its utilities and ease of use (formalized in the TAM). The second is the degree of acceptance decreases with the user's perception of threat or affirmation posed by the technology in relation to social norms and values (formalized in the Moral Foundation Theory). This study begins by mapping the ecology of current emotional AI use in various contexts such as workplace, education, healthcare, personal assistance, etc. It then provides a brief review and critique of current applications of the TAM and the Moral Foundation Theory in studying how humans judge smart technologies. Finally, we propose the Three-pronged Analytical Framework, offering recommendations on how future studies of technological acceptance could be conducted from the questionnaire design to building statistical models.
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The paper proposes Bayesian analysis as an alternative approach for the conventional frequentist approach in analyzing social data. A step-by-step protocol of how to implement Bayesian multilevel ...model analysis with social data and how to interpret the result is presented. The article used a dataset regarding religious teachings and behaviors of lying and violence as an example. An analysis is performed using R statistical software and a bayesvl R package, which offers a network-structured model construction and visualization power to diagnose and estimate results.•The paper provides guidance for conducting a Bayesian multilevel analysis in social sciences through constructing directed acyclic graphs (DAGs, or "relationship trees") for different models, basic and more complex ones.•The method also illustrates how to visualize Bayesian diagnoses and simulated posterior.•The interpretations of visualized diagnoses and simulated posteriors of Bayesian inference are also discussed.
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We have examined the attitude and moral perception of 228 college students (63 Japanese and 165 non-Japanese) towards artificial intelligence (AI) in an international university in Japan. The ...students were asked to select a single most significant ethical issue associated with AI in the future from a list of nine ethical issues suggested by the World Economic Forum, and to explain why they believed that their chosen issues were most important. The majority of students (
n
= 149, 65%) chose unemployment as the major ethical issue related to AI. The second largest group of students (
n
= 29, 13%) were concerned with ethical issues related to emotional AI, including the impact of AI on human behavior and emotion. The paper discusses the results in detail and concludes that, while policymakers must consider how to ameliorate the impact of AI on employment, AI engineers need to consider the emotional aspects of AI in research and development, as well.
To evaluate the moral awareness of college students regarding artificial intelligence (AI) systems, we have examined 467 surveys collected from 152 Japanese and 315 non-Japanese students in an ...international university in Japan. The students were asked to choose a most significant moral problem of AI applications in the future from a list of ten ethical issues and to write an essay about it. The results show that most of the students (n = 269, 58%) considered unemployment to be the major ethical issue related to AI. The second largest group of students (n = 54, 12%) was concerned with ethical issues related to emotional AI, including the impact of AI on human behavior and emotion and robots’ rights and emotions. A relatively small number of students referred to the risk of social control by AI (6%), AI discrimination (6%), increasing inequality (5%), loss of privacy (4%), AI mistakes (3%), malicious AI (3%), and AI security breaches (3%). Calculation of the
z
score for two population proportions shows that Japanese students were much less concerned about AI control of society (− 3.1276,
p
< 0.01) than non-Japanese students, but more concerned about discrimination (2.2757,
p
< 0.05). Female students were less concerned about unemployment (− 2.6108,
p
< 0.01) than males, but more concerned about discrimination (2.4333,
p
< 0.05). The study concludes that the moral awareness of college students regarding AI technologies is quite limited and recommends including the ethics of AI in the curriculum.
Stress and depression can be seen as the major obstacles for sustained education and attainment of foreign students, and in turn, the sustainability of an education system as a whole. However, the ...mainstream consideration following Berry’s model on acculturation does not take into account whether students of the host countries are immune to these problems. This study aims to examine the prevalence and predictors of help-seeking behaviors among international and domestic students in a multicultural environment by employing ANOVA and polynomial regression. Some significant results from this study are: (1) Informal sources were the most prevalent sources of help-seeking among international and domestic students, while formal help-seeking was not popular; (2) international students were more likely to overcome emotional difficulties alone and seek help on the Internet than domestic students; (3) acculturative stress was a positive predictor of formal, informal, and miscellaneous help-seeking behaviors among international students and informal help-seeking behaviors or among domestic students; and (4) depression was negatively correlated with the willingness of international students to seek help from informal sources. The findings hint at the risk of acculturative stress faced by domestic students in a multicultural environment being overlooked and the lack of help-seeking sources for international students. The study also provides empirical evidence for policy-planners to design a sustainable education system better at supporting students dealing with depression and acculturative stress.
This review paper presents a framework to evaluate the artificial intelligence (AI) readiness for the healthcare sector in developing countries: a combination of adequate technical or technological ...expertise, financial sustainability, and socio-political commitment embedded in a healthy psycho-cultural context could bring about the smooth transitioning toward an AI-powered healthcare sector. Taking the Vietnamese healthcare sector as a case study, this paper attempts to clarify the negative and positive influencers. With only about 1500 publications about AI from 1998 to 2017 according to the latest Elsevier AI report, Vietnamese physicians are still capable of applying the state-of-the-art AI techniques in their research. However, a deeper look at the funding sources suggests a lack of socio-political commitment, hence the financial sustainability, to advance the field. The AI readiness in Vietnam's healthcare also suffers from the unprepared information infrastructure-using text mining for the official annual reports from 2012 to 2016 of the Ministry of Health
the paper found that the frequency of the word "database" actually decreases from 2012 to 2016, and the word has a high probability to accompany words such as "lacking", "standardizing", "inefficient", and "inaccurate." Finally, manifestations of psycho-cultural elements such as the public's mistaken views on AI or the non-transparent, inflexible and redundant of Vietnamese organizational structures can impede the transition to an AI-powered healthcare sector.
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