AI, ADR, AND ANXIETY

This post discusses AI generally, growing anxiety about it and modern life generally, and how we can manage this anxiety. Anxiety about AI may be feeding into a more general anxiety about events in the US and around the world. We can address anxiety by focusing on what we actually can control. Regarding AI and ADR, I suggest that the machine mediation “glass” will be partly empty and partly full – as is human mediation. It’s important to recognize our own reactions to and fears about AI, have as accurate and balanced an understanding of what’s happening as possible, acknowledge th

Why Do Family Members Reject AI in Health Care? Competing Effects of Emotions

Artificial intelligence (AI) enables continuous monitoring of patients’ health, thus improving the quality of their health care. However, prior studies suggest that individuals resist such innovative technology. In contrast to prior studies that investigate individuals’ decisions for themselves, we focus on family members’ rejection of AI monitoring, as family members play a significant role in health care decisions. Our research investigates competing effects of emotions toward the rejection of AI monitoring for health care. Based on two scenario-based experiments, our study reveals that emotions play a decisive role in family members’ decision making on behalf of their parents. We find that anxiety about health care monitoring and anxiety about health outcomes reduce the rejection of AI monitoring, whereas surveillance anxiety and delegation anxiety increase rejection. We also find that for individual-level risks, perceived controllability moderates the relationship between surveillance anxiety and the rejection of AI monitoring. We contribute to the theory of Information System rejection by identifying the competing roles of emotions in AI monitoring decision making. We extend the literature on decision making for others by suggesting the influential role of anxiety. We also contribute to healthcare research in Information System by identifying the important role of controllability, a design factor, in AI monitoring rejection

Why do Family Members Reject AI in Health Care? Competing Effects of Emotions

Artificial intelligence (AI) enables continuous monitoring of patients’ health, thus improving the quality of their health care. However, prior studies suggest that individuals resist such innovative technology. In contrast to prior studies that investigate individuals’ decisions for themselves, we focus on family members’ rejection of AI monitoring, as family members play a significant role in health care decisions. Our research investigates competing effects of emotions toward the rejection of AI monitoring for health care. Based on two scenario-based experiments, our study reveals that emotions play a decisive role in family members’ decision making on behalf of their parents. We find that anxiety about health care monitoring and anxiety about health outcomes reduce the rejection of AI monitoring, whereas surveillance anxiety and delegation anxiety increase rejection. We also find that for individual-level risks, perceived controllability moderates the relationship between surveillance anxiety and the rejection of AI monitoring. We contribute to the theory of Information System rejection by identifying the competing roles of emotions in AI monitoring decision making. We extend the literature on decision making for others by suggesting the influential role of anxiety. We also contribute to healthcare research in Information System by identifying the important role of controllability, a design factor, in AI monitoring rejection

Existential anxiety about artificial intelligence (AI)- is it the end of humanity era or a new chapter in the human revolution: questionnaire-based observational study

Background: Existential anxiety can profoundly affect an individual, influencing their perceptions, behaviours, sense of well-being, academic performance, and decisions. Integrating artificial intelligence into society has elicited complex public reactions, marked by appreciation and concern, with its acceptance varying across demographics and influenced by factors such as age, gender, and prior AI experiences. This study aimed to investigate the existential anxiety about artificial intelligence (AI) in public in Saudi Arabia. Methods: The present questionnaire-based observational, analytical cross-sectional study with a structured, self-administered survey was conducted via Google Forms, using a scale to assess the existential anxiety levels induced by the recent development of AI. The study encompassed a diverse population with a sample size of 300 participants. Results: This study’s findings revealed a high prevalence of existential anxieties related to the rapid advancements in AI. Key concerns included the fear of death (96% of participants), fate’s unpredictability (86.3%), a sense of emptiness (79%), anxiety about meaninglessness (92.7%), guilt over potential AI-related catastrophes (87.7%), and fear of condemnation due to ethical dilemmas in AI (93%), highlighting widespread apprehensions about humanity’s future in an AI-dominated era. Conclusion: The public has concerns including unpredictability, a sense of emptiness, anxiety, guilt over potential AI-related catastrophes, and fear of condemnation due to ethical dilemmas in AI, highlighting widespread apprehensions about humanity’s future in an AI-dominated era. The results indicate that there is a need for a multidisciplinary strategy to address the existential anxieties in the AI era. The strategic approach must blend technological advancements with psychological, philosophical, and ethical insights, underscoring the significance of human values in an increasingly technology-driven world

Wearable artificial intelligence for anxiety and depression: A scoping review

Background: Anxiety and depression are the most common mental disorders worldwide. Owing to the lack of psychiatrists around the world, the incorporation of AI and wearable devices (wearable artificial intelligence (AI)) have been exploited to provide mental health services. Objective: The current review aimed to explore the features of wearable AI used for anxiety and depression to identify application areas and open research issues. Methods: We searched 8 electronic databases (MEDLINE, PsycINFO, EMBASE, CINAHL, IEEE Xplore, ACM Digital Library, Scopus, and Google Scholar). Then, we checked studies that cited the included studies, and screened studies that were cited by the included studies. Study selection and data extraction were carried out by two reviewers independently. The extracted data were aggregated and summarized using the narrative synthesis. Results: Of the 1203 citations identified, 69 studies were included in this review. About two thirds of the studies used wearable AI for depression while the remaining studies used it for anxiety. The most frequent application of wearable AI was diagnosing anxiety and depression while no studies used it for treatment purposes. The majority of studies targeted individuals between the ages of 18 and 65. The most common wearable devices used in the studies were Actiwatch AW4. The wrist-worn devices were most common in the studies. The most commonly used data for model development were physical activity data, sleep data, and heart rate data. The most frequently used dataset from open sources was Depresjon. The most commonly used algorithms were Random Forest (RF) and Support Vector Machine (SVM). Conclusions: Wearable AI can offer great promise in providing mental health services related to anxiety and depression. Wearable AI can be used by individuals as a pre-screening assessment of anxiety and depression. Further reviews are needed to statistically synthesize studies’ results related to the performance and effectiveness of wearable AI. Given its potential, tech companies should invest more in wearable AI for treatment purposes for anxiety and depression

The roles of personality traits, AI anxiety, and demographic factors in attitudes towards artificial intelligence

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Human-Computer Interaction on 07/12/2022, available online: https://doi.org/10.1080/10447318.2022.2151730The present study adapted the General Attitudes toward Artificial Intelligence Scale (GAAIS) to Turkish and investigated the impact of personality traits, artificial intelligence anxiety, and demographics on attitudes toward artificial intelligence. The sample consisted of 259 female (74%) and 91 male (26%) individuals aged between 18 and 51 (Mean = 24.23). Measures taken were demographics, the Ten-Item Personality Inventory, the Artificial Intelligence Anxiety Scale, and the General Attitudes toward Artificial Intelligence Scale. The Turkish GAAIS had good validity and reliability. Hierarchical Multiple Linear Regression Analyses showed that positive attitudes toward artificial intelligence were significantly predicted by the level of computer use (β = 0.139, p = 0.013), level of knowledge about artificial intelligence (β = 0.119, p = 0.029), and AI learning anxiety (β = −0.172, p = 0.004). Negative attitudes toward artificial intelligence were significantly predicted by agreeableness (β = 0.120, p = 0.019), AI configuration anxiety (β = −0.379, p < 0.001), and AI learning anxiety (β = −0.211, p < 0.001). Personality traits, AI anxiety, and demographics play important roles in attitudes toward AI. Results are discussed in light of the previous research and theoretical explanations

Why Do People Fear AI? Let’s Talk Morality

Artificial Intelligence (AI) may be the best thing to happen to humanity, or it may be the worst thing (Hern, 2016). The negative propaganda and demonization of AI and its many “threats” to humanity have induced certain fears towards AI. The aim of this research is to analyze the fear of AI which we refer to as AI anxiety-from a moral lens by understanding how an individual’s moral foundations led to their AI anxiety. This paper borrows from the moral psychology literature and implements Moral Foundation Theory into Information Systems (IS) research for the first time

A Rat Model of Post-Traumatic Stress Syndrome Causes Phenotype-Associated Morphological Changes and Hypofunction of the Adrenal Gland

BACKGROUND Rats exposed to chronic predator scent stress mimic the phenotype of complex post-traumatic stress disorder (PTSD) in humans, including altered adrenal morphology and function. High- and low-anxiety phenotypes have been described in rats exposed to predator scent stress (PSS). This study aimed to determine whether these high- and low-anxiety phenotypes correlate with changes in adrenal histomorphology and corticosteroid production. METHODS Rats were exposed to PSS for ten days. Thirty days later, the rats' anxiety index (AI) was assessed with an elevated plus-maze test. Based on differences in AI, the rats were segregated into low- (AI ≤ 0.8, n = 9) and high- (AI > 0.8, n = 10) anxiety phenotypes. Plasma corticosterone (CORT) concentrations were measured by ELISA. Adrenal CORT, desoxyCORT, and 11-dehydroCORT were measured by high-performance liquid chromatography. After staining with hematoxylin and eosin, adrenal histomorphometric changes were evaluated by measuring the thickness of the functional zones of the adrenal cortex. RESULTS Decreased plasma CORT concentrations, as well as decreased adrenal CORT, desoxyCORT and 11-dehydroCORT concentrations, were observed in high- but not in low-anxiety phenotypes. These decreases were associated with increases in AI. PSS led to a significant decrease in the thickness of the zona fasciculata and an increase in the thickness of the zona intermedia. The increase in the thickness of the zona intermedia was more pronounced in low-anxiety than in high-anxiety rats. A decrease in the adrenal capsule thickness was observed only in low-anxiety rats. The nucleus diameter of cells in the zona fasciculata of high-anxiety rats was significantly smaller than that of control or low-anxiety rats. CONCLUSION Phenotype-associated changes in adrenal function and histomorphology were observed in a rat model of complex post-traumatic stress disorder

AN ADAPTATION OF ARTIFICIAL INTELLIGENCE ANXIETY SCALE INTO TURKISH: RELIABILITY AND VALIDITY STUDY

The widespread use of artificial intelligence (AI) has been growing in various fields. AI is defined as human-like automation in place of human beings that can operate many functions based on some level of intelligence. In education, AI offers powerful pedagogical tools that can help enhance instructional quality. Given the inevitable advancements of AI in education, this study aims to investigate teachers’ AI anxiety levels based on various demographic factors. For this purpose, the AI Anxiety Scale is adapted into Turkish, which provides a good fit of the model to the data for the construct validity. Moreover, the reliability coefficients of the scale show strong evidence of consistency in teachers’ responses to the items. For sociotechnical blindness dimension, male and female teachers do not show any significant differences. However, for learning, job replacement, AI configuration dimensions and the total scale, female teachers are more anxious towards AI than male teachers. Moreover, there is no difference observed based on degree levels teachers hold. Additionally, anxiety levels of teachers are not related to teachers’ age and years of experience in teaching

A Rat Model of Post-Traumatic Stress Syndrome Causes Phenotype-Associated Morphological Changes and Hypofunction of the Adrenal Gland

Background: Rats exposed to chronic predator scent stress mimic the phenotype of complex post-traumatic stress disorder (PTSD) in humans, including altered adrenal morphology and function. High- and low-anxiety phenotypes have been described in rats exposed to predator scent stress (PSS). This study aimed to determine whether these high- and low-anxiety phenotypes correlate with changes in adrenal histomorphology and corticosteroid production. Methods: Rats were exposed to PSS for ten days. Thirty days later, the rats’ anxiety index (AI) was assessed with an elevated plus-maze test. Based on differences in AI, the rats were segregated into low- (AI ≤ 0.8, n = 9) and high- (AI > 0.8, n = 10) anxiety phenotypes. Plasma corticosterone (CORT) concentrations were measured by ELISA. Adrenal CORT, desoxyCORT, and 11-dehydroCORT were measured by high-performance liquid chromatography. After staining with hematoxylin and eosin, adrenal histomorphometric changes were evaluated by measuring the thickness of the functional zones of the adrenal cortex. Results: Decreased plasma CORT concentrations, as well as decreased adrenal CORT, desoxyCORT and 11-dehydroCORT concentrations, were observed in high- but not in low-anxietyphenotypes. These decreases were associated with increases in AI. PSS led to a significant decrease in the thickness of the zona fasciculata and an increase in the thickness of the zona intermedia. The increase in the thickness of the zona intermedia was more pronounced in low-anxiety than in high-anxiety rats. A decrease in the adrenal capsule thickness was observed only in low-anxiety rats. The nucleus diameter of cells in the zona fasciculata of high-anxiety rats was significantly smaller than that of control or low-anxiety rats. Conclusion: Phenotype-associated changes in adrenal function and histomorphology were observed in a rat model of complex post-traumatic stress disorder