Emotional Artificial Intelligence Research Articles

The Effect of Emotional Intelligence on Higher Education: A Pilot Study on the interplay Between Artificial Intelligence, Emotional Intelligence, and E-Learning

Integrating Artificial Intelligence (AI) and E-learning platforms has become increasingly prevalent in the rapidly evolving landscape of higher Education. However, amidst this technological advancement, the role of Emotional Intelligence (EI) and its impact on the efficacy of AI-driven educational tools still needs to be explored. This pilot study seeks to elucidate the intricate relationship between Emotional Intelligence, Artificial Intelligence, and E-Learning in Higher Education. Drawing upon a multidisciplinary approach, this study investigates the correlation between students' Emotional Intelligence competencies and their engagement with AI-driven E-Learning platforms. The findings of this pilot study are expected to shed light on several critical aspects. Firstly, it aims to uncover how Emotional Intelligence influences students' receptivity to AI-infused E-Learning environments, potentially elucidating strategies for optimizing user experience and learning outcomes. Moreover, by exploring the reciprocal influence between Emotional Intelligence and AI algorithms, this research endeavors to contribute to the refinement of AI technologies, fostering greater personalization and adaptability in educational settings. Furthermore, this study endeavors to address the ethical implications inherent in the intersection of Emotional Intelligence, Artificial Intelligence, and E-Learning. By elucidating the potential risks and benefits associated with integrating these technologies, it seeks to inform policymakers, educators, and AI developers alike, facilitating the responsible deployment of AI-driven educational tools. Therefore, its innovative methodology and comprehensive approach aspire to pave the way for future research endeavors, ultimately enriching the educational landscape with insights prioritizing technological advancement and human well-being.

Эмотикон как пример развития графической эмотиологии в виртуальном пространстве

Emoticons are a vivid example of graphic emotionology in virtual communication. The interdisciplinary character of this research made it possible to combine linguistics with exact and natural sciences, including the young science of emotional artificial intelligence. Transmitting psycho-emotional experiences is important for effective virtual communication. Emotional pictography provides a more accurate understanding of the emotive content in a message. It relies on associative links between the image of a specific psycho-emotional state and its perception by a carrier of a specific linguistic culture. Basic emoticons are of universal nature, and internet users with different cultural and linguistic backgrounds tend to interpret them similarly. However, members of different ethno-linguistic communities sometimes decode the same emotions differently, which may lead to misunderstanding, as well as to serve as a marker on the friend – foe axis. Emoticons represent a clearly verified range of emotive states, which can be appealed to in virtual communication. The authors developed a classification of emoticons and proposed graphic emotionology as a new branch of linguistic semiotics. The metaphorical nature of the emoticon as a coded emotion with additional evaluative shades is revealed as a certain unified image or a complex of signs due to the accepted emotive norms of speech behavior. In addition, emoticons provide efficient compression during virtual communication. Intercultural virtual communication enriches the range of emoticons both linguistically and culturally, thus developing graphic emotionology internationally. Emoticons provide insight into the development of graphic emotionology.

A systematic review of research on emotional artificial intelligence in English language education

In learning English as a foreign language (EFL), students often experience foreign language anxiety. Artificial intelligence (AI) applications that provide emotional support and/or create emotional impacts on student learning, so-called emotional AI applications, have received increased attention. However, there is a lack of a systematic review of studies on emotional AI in EFL education. This paper presents a systematic review of research in this field. The results reveal five affordances of emotional AI in EFL education, namely (1) enabling human-like conversations, (2) providing personalized real-time feedback or instructions, (3) translating images into English text, (4) generating personalized learning content and tasks, and (5) recognizing and analyzing emotions. The first three affordances are more frequently used and have shown promising effects on improving students’ behavioral, cognitive, and affective learning outcomes. Moreover, the findings reveal that emotional support is often integrated with cognitive support; providing emotional support alone may not be enough to support student learning. Meanwhile, providing cognitive support alone can enhance both affective and cognitive learning outcomes. Finally, attention should be paid to the factors that might influence the adoption and effects of emotional AI in EFL education.

A research on copyright issues impacting artists emotional states in the framework of artificial intelligence.

Art and artistic creation serve as a means for artists to communicate with their environment, society, and the external world. However, the protection of artistic creations, as forms of communication, is not only a right for artists but also serves as a crucial safeguard that nurtures them during the creative process. Beyond the traditional issues of copyright, the significant advancements in Artificial Intelligence (AI) in today's digital world have introduced a new debate regarding the ownership of copyright in artistic creations generated by AI. The question arises whether copyright belongs to the AI itself or to the individuals who guide the creative process behind it. In this study, based on the concepts of art, artistic creation, and emotional states, copyright issues will be examined. Data obtained from semi-structured in-depth interviews with artists and academic experts (eight artists, two communication experts, two law experts, and eight psychology experts) in the field will be analysed through content analysis to explore their perspectives regarding the discussion on emotional states, AI, and copyrights. The research highlights the variability of emotional states and their significant effects on individuals. Addressing the increasing trend of copyright issues, particularly within the framework of digitalization and inadequate legal regulations, it was found that artists' emotional states are negatively impacted by these problems. This negative influence can adversely affect artists' creativity and desire to produce. On the other hand, it was also identified that in artworks produced especially through AI, if artists' rights are not protected, there is a possibility of negative emotional states arising. In conclusion, suggestions are as follows: Emphasising the importance of awareness-raising educational activities nationally and internationally, national copyright law (in Northern Cyprus) needs to be revised to protect traditional copyright and be expanded to include digital copyright, especially for works produced through AI. On an international level, emphasising the need to revise international agreements to include regulations for works produced through AI or to create a new agreement based on the importance of this issue.

Emotional AI in education and toys: Investigating moral risk awareness in the acceptance of AI technologies from a cross-sectional survey of the Japanese population

Emotional artificial intelligence (AI), i.e., affective computing technologies, is rapidly reshaping the education of young minds worldwide. In Japan, government and commercial stakeholders are promulgating emotional AI not only as a neoliberal, cost-saving benefit but also as a heuristic that can improve the learning experience at home and in the classroom. Nevertheless, critics warn of a myriad of risks and harms posed by the technology such as privacy violation, unresolved deeper cultural and systemic issues, machinic parentalism as well as the danger of imposing attitudinal conformity. This study brings together the Technological Acceptance Model and Moral Foundation Theory to examine the cultural construal of risks and rewards regarding the application of emotional AI technologies. It explores Japanese citizens’ perceptions of emotional AI in education and children's toys via analysis of a final sample of 2000 Japanese respondents with five age groups (20s–60s) and two sexes equally represented. The linear regression models for determinants of attitude toward emotional AI in education and in toys account for 44 % and 38 % variation in the data, respectively. The analyses reveal a significant negative correlation between attitudes toward emotional AI in both schools and toys and concerns about privacy violations or the dystopian nature of constantly monitoring of children and students’ emotions with AI (Education: βDystopianConcern = − .094***; Toys: βPrivacyConcern = − .199***). However, worries about autonomy and bias show mixed results, which hints at certain cultural nuances of values in a Japanese context and how new the technologies are. Concurring with the empirical literature on the Moral Foundation Theory, the chi-square (Χ2) test shows Japanese female respondents express more fear regarding the potential harms of emotional AI technologies for the youth's privacy, autonomy, data misuse, and fairness (p < 0.001). The policy implications of these results and insights on the impacts of emotional AI for the future of human-machine interaction are also provided.

A survey of Emotional Artificial Intelligence and crimes: detection, prediction, challenges and future direction

A survey of Emotional Artificial Intelligence and crimes: detection, prediction, challenges and future direction

Sentient libraries: empowering user expeditions with emotional artificial intelligence

Purpose The study aims to forecast emotional artificial intelligence’s (EAI) trajectory in libraries, provide actionable implementation recommendations and outline potential research paths. It seeks to establish ethical benchmarks, considering emotional well-being alongside informational needs. Understanding EAI in libraries becomes pivotal, envisioning libraries as empathetic, user-centric spaces. Design/methodology/approach This research uses a multifaceted approach, intertwining comprehensive literature analysis, illustrative case studies and forward-thinking assessments. It combines foundational knowledge from various academic sources, integrating real-world examples of EAI implementations in library settings. The methodology goes beyond observational analysis, scrutinizing ethical quandaries and societal impacts. It comprehensively explains EAI’s role while delineating guidelines for responsible integration and future exploration. Findings Integrating emotional intelligence (EI) enhances resource curation, fosters inclusive virtual learning environments and elevates continuous service enhancement in libraries. It allows tailored recommendations, emotionally perceptive virtual assistants and adaptive responses to diverse emotional needs, reshaping libraries into empathetic spaces. Research limitations/implications Ethical considerations pose significant challenges, especially regarding privacy, security and fair usage of EAI. Libraries must mitigate biases and ensure equitable emotional services. Adapting to diverse emotional needs within library services requires a delicate balance between empathetic approaches and upholding ethical standards. Practical implications EAI revolutionizes user experiences, offering personalized, engaging interactions while ensuring ethical, inclusive library environments. Implementing EAI requires navigating biases and ethical complexities, demanding continuous refinement and using user-focused strategies. Social implications Integrating EAI redefines the library’s role, transforming libraries into emotionally perceptive hubs catering to users’ intellectual and emotional needs. It emphasizes fairness, trust and ethical conduct in providing empathetic, inclusive services. Originality/value This research pioneers the integration of EI in libraries, offering insights into ethical frameworks, bias mitigation and user-centric approaches. It envisions libraries as empathetic, responsive spaces, enriching lives through emotional understanding and responsiveness.

Exploring Emotional Intelligence in Jordan’s Artificial Intelligence (AI) Healthcare Adoption: A UTAUT Framework

The integration of Artificial Intelligence (AI) has been reshaping healthcare globally. However, the AI adoption in Jordan is met with cautious progress. AI has shown substantial potential to enhance healthcare services and foster Emotional Intelligence (EI), especially in advanced economies. Despite its proven effectiveness elsewhere, the Jordanian populace is reluctant to adopt AI in the healthcare sector, with predictions for hospitalizations, medical consultations, and treatment recommendations being sluggish to gain acceptance. This study investigates the combination of Emotional Intelligence and AI adoption in the healthcare system in Jordan, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) model. While UTAUT typically considers performance expectancy, effort expectancy, social influence, and facilitating conditions as key determinants of technology acceptance, this study argues that emotional intelligence, including self-regulated, self-awareness, motivation, empathy, and social skills, should be integrated as direct determinants of behavioural intention. In this study, a quantitative approach has been employed, whereby questionnaires were delivered through email and messaging apps to evaluate the impact of emotional intelligence on Jordanians’ willingness to adopt AI technology in the healthcare sector. The findings suggested that the UTAUT model should be further expanded to encompass emotional intelligence as its fifth construct, particularly in developing countries like Jordan, where user models for AI adoption are less explored. The implications of the study extend to healthcare planners and developers in Jordan, providing insights into factors, which influence the successful adoption of AI technologies among diverse user groups. This study has provided valuable recommendations for developers of AI-based healthcare systems, enabling them to align their assistance with the perceptions and behaviours of Middle Eastern users. By doing so, they can foster increased acceptance of AI-based healthcare systems in the Middle East and other developing regions to improve healthcare services.

Knowledge and Awareness of Emotional Artificial Intelligence as Tool in Child’s Oral Health Care Assessed Among Dental Professionals of Eastern India. A Cross Sectional Study

ABSTRACT Background: One of the most crucial steps in providing the child’s oral health care and treatment needs is modifying his/her behavior, mood and emotion in any primary care/dental home or any speciality centre. This is very important parameters for any dental professionals and paediatric dentist. Through this study we investigated the knowledge, awareness of Emotional Artificial Intelligence (AI) as tool for modifying their behaviour prior to any treatment requirement. Materials and Methods: The study was conducted among dental professionals of located in different regions of eastern India. We used a consecutive sampling method to collect our sample. Results: Out of the 120 participating dental professionals, 30% professionals believed that emotional AI could monitor a child’s acceptance to treatment. Despite a low perception level of emotional AI by the dentist, 71% agreed on the beneficial effect of Emotional Artificial Intelligence (AI) in an emergency or uncooperative child if such technology exist. Conclusion: Regression model showed that certain key influencers can increase the quality of healthcare by 3 times which could favors Emotional Artificial Intelligence as an adjunct to Pediatric dentists in managing children.

On manipulation by emotional AI: UK adults' views and governance implications.

With growing commercial, regulatory and scholarly interest in use of Artificial Intelligence (AI) to profile and interact with human emotion ("emotional AI"), attention is turning to its capacity for manipulating people, relating to factors impacting on a person's decisions and behavior. Given prior social disquiet about AI and profiling technologies, surprisingly little is known on people's views on the benefits and harms of emotional AI technologies, especially their capacity for manipulation. This matters because regulators of AI (such as in the European Union and the UK) wish to stimulate AI innovation, minimize harms and build public trust in these systems, but to do so they should understand the public's expectations. Addressing this, we ascertain UK adults' perspectives on the potential of emotional AI technologies for manipulating people through a two-stage study. Stage One (the qualitative phase) uses design fiction principles to generate adequate understanding and informed discussion in 10 focus groups with diverse participants (n = 46) on how emotional AI technologies may be used in a range of mundane, everyday settings. The focus groups primarily flagged concerns about manipulation in two settings: emotion profiling in social media (involving deepfakes, false information and conspiracy theories), and emotion profiling in child oriented "emotoys" (where the toy responds to the child's facial and verbal expressions). In both these settings, participants express concerns that emotion profiling covertly exploits users' cognitive or affective weaknesses and vulnerabilities; additionally, in the social media setting, participants express concerns that emotion profiling damages people's capacity for rational thought and action. To explore these insights at a larger scale, Stage Two (the quantitative phase), conducts a UK-wide, demographically representative national survey (n = 2,068) on attitudes toward emotional AI. Taking care to avoid leading and dystopian framings of emotional AI, we find that large majorities express concern about the potential for being manipulated through social media and emotoys. In addition to signaling need for civic protections and practical means of ensuring trust in emerging technologies, the research also leads us to provide a policy-friendly subdivision of what is meant by manipulation through emotional AI and related technologies.

Emotional Artificial Intelligence: Methodologies, Benefits, and Drawbacks

Abstract: This survey paper explores various research studies and papers related to Emotional Artificial Intelligence (Emotion AI). Emotion AI has gained significant attention in recent years due to its potential applications in diverse fields, such as mental health, workplace surveillance, education, and music generation. The paper provides an overview of the methodologies employed in these studies, along with the benefits and drawbacks associated with each approach. By examining the literature, this paper aims to shed light on the current state of Emotional AI and its implications for different domains

Exploring the Impact of Convolutional Neural Networks on Facial Emotion Detection and Recognition

Emotional analytics is a fascinating blend of psychology and technology, with one of the primary methods for recognizing emotions involving facial expression analysis. Facial emotion detection has advanced significantly, utilizing deep learning algorithms to identify common emotions. In recent years, substantial progress has been made in automatic facial emotion recognition (FER). This technology has been applied across various industries to enhance interactions between humans and machines, particularly in human-centered computing and the emerging field of emotional artificial intelligence (EAI). Researchers focus on improving systems’ capabilities to recognize and interpret human facial expressions and behaviors in diverse contexts. The impact of convolutional neural networks (CNNs) on this field has been profound, as these networks have undergone significant development, leading to diverse architectures designed to address increasingly complex challenges. This article explores the latest advancements in automated emotion recognition using computational intelligence, emphasizing how contemporary deep learning models contribute to the field. It provides a review of recent developments in CNN architectures for FER over the past decade, demonstrating how deep learning-based methods and specialized databases collaborate to achieve highly accurate outcomes.

Legal incentives for innovations in the emotional AI domain: a carrot and stick approach?

Abstract Emotions strongly influence the human way of living and life experiences. In this context, Artificial Intelligence (AI) technologies are crucial to pushing developments further. Although emotional AI-driven innovations are welcome in our society, they might also have negative effects on the interdependence and autonomy of natural persons. Thus, they might be challenged by several legal provisions in the EU such as the General Data Protection Regulation (GDPR) and the draft AI Act. Yet these inventions require considerable investment, where legal incentives such as intellectual property rights (IPR) are crucial. Indeed, it is also important to secure certainty as to the legal and ethical acceptability of such innovations. This article looks at emotional AI to investigate the interlinkage between technological innovations, legal incentives and ethics, through the lenses of patent law and fundamental rights, in order to shed light over the challenges, limitations, but also opportunities for the protection, commercialization and exploitation of emotional AI-related inventions. Our research offers new scientific knowledge on the largely under-explored issue of legal incentives for emotional AI-related inventions in the European framework. It also provides companies and inventors with key points to consider in decision-making related to investments in and incentives for emotional AI-related innovations, also elaborating on suggestions for the European legislator and policymakers to better stimulate and promote emotional AI technology through regulation.

The Infopolitics of feeling: How race and disability are configured in Emotion Recognition Technology

In this article, we argue that facial emotion recognition technology (facial ERT) reproduces historical forms of pseudoscience based on the concept of quantifiable and unequally distributed emotional capacity. Drawing on Kyla Schuller’s Biopolitics of Feeling and Colin Koopman’s theory of infopower, we put forward the term ‘the infopolitics of feeling’ to describe how facial ERT encodes culturally ‘correct’ or normative forms of emotional expression that have historically been used to define and delineate what it means to be human. To make this argument, we provide a close reading of Girl Decoded, the autobiography of Rana el Kaliouby, the founder and former CEO of the leading Emotion artificial intelligence (AI) firm Affectiva. Girl Decoded, we argue pits el Kaliouby herself – portrayed as the empathetic, liberal, emotionally expressive and ideal ‘feeling’ subject – against two non-normative figures: the unfeeling autist and the inscrutable Oriental who must be ‘cured’ through Affectiva’s facial ERT.

An Emotional Artificial Intelligence Based Attendance System for Class Room

Attendance through a camera using Artificial Intelligence (AI) and Deep Learning (DL) is a mod-ern approach to monitoring and tracking attendance in various settings such as schools, offices, and man-ufacturing facilities. It uses computer vision techniques and deep learning algorithms to automatically detect and identify individuals in an image or video captured by the camera. The process typically in-volves training the system on a dataset of labelled images of the individuals who will be taking attend-ance. This training data can include images of the individual's face, iris, or fingerprints, depending on the specific approach used. Once the system is trained, it can then use this knowledge to recognize these in-dividuals in new images captured by the camera. When an individual is recognized, the system can log their attendance in a database or other record-keeping system. This can be done in real-time, allowing for immediate tracking of attendance, or it can be done at a later time for batch processing. However, AI has been expanding its horizon and face recognition with deep learning techniques can be augmented with emotion recognition as well. Sometimes, students feel really sad and overwhelmed by their school work and other responsibilities. They might feel like they can't keep up or that they are not good enough. When these feelings last for a long time, it's called depression. Depression can make it hard for students to do their school work, be with friends and family, or even get out of bed in the morning. When students are feeling really sad and hopeless, they might think about hurting themselves or ending their lives. This is called suicide. It's important to know that suicide is preventable and there are people who can help. It's important to take care of our mental health, just like we take care of our physical health. If you ever feel sad or overwhelmed, it's important to talk to someone you trust and get help. One way to prevent suicide is to detect if someone is feeling sad and hopeless, which can be a sign of depression. One way to do this is by using a CCTV camera and AI and DL technology to analyze the person's facial expression, body language, and speech patterns. Emotional AI and DL can be a powerful tool in detecting depression but it's not a substitute for a professional diagnosis. It's always advisable to consult with a mental health pro-fessional if you suspect that you or someone you know may be struggling with depression.

Equipping AI-decision-support-systems with emotional capabilities? Ethical perspectives.

It is important to accompany the research on Emotional Artificial Intelligence with ethical oversight. Previous publications on the ethics of Emotional Artificial Intelligence emphasize the importance of subjecting every (possible) type of Emotional Artificial Intelligence to separate ethical considerations. That's why, in this contribution I will focus on a particular subset of AI systems: AI-driven Decision-Support Systems (AI-DSS), and ask whether it would be advisable from an ethical perspective to equip these AI systems with emotional capacities. I will show, on one hand, equipping AI-DSS with emotional capabilities offers great opportunities, as they open the possibility to prevent emotionally biased decisions - but that it also amplifies the ethical challenges already posed by emotionally-incapable AI-DSS. Yet, if their introduction is accompanied by a broad social discourse and prepared by suitable measures to address these challenges, I argue, nothing should fundamentally stand in the way of equipping AI-DSS with emotional capabilities.

Exploring the Direction of English Translation of Environmental Articles Based on Emotional Artificial Intelligence Learning Models

Abstract In this paper, we improve the performance of machine translation emotion recognition by fusing word features and linguistic features of environmentally friendly text with LSTM neural networks. Multi-channel LSTM’s unbalanced emotion classification method can address the problem of unbalanced numbers of samples in each emotion category in the emotion classification task. The bilingual semantic similarity degree feature is added to the translation process of English environmental protection data so as to make the translation of English environmental protection data more accurate. The proposed method is validated through experiments, and the results show that the translation quality of the LSTM model is generally higher than that of other models, and the mean value of the translation quality of the LSTM on the test17 dataset is 50.4. In terms of environmental protection terminology, mistranslations, common word mistranslations, etc. There is a significant reduction of 2–3 times in the absolute number of the LSTM model compared to the traditional method. A total of 94,788 corpora were obtained from 10 environmental translations, such as Silent Spring and Solace of the Heart, and 2,311 favorite emotion words were identified. Comparative analysis of the samples reveals that most of the translation results of some environmental terms are in the range of 0.7–0.9, and the range of semantic overlay between the machine translation results of the LSTM model and the standard translations is large. Most users believe the model improves the translator’s translation level and diction accuracy.

Improvement of emotion recognition methods based on neural networks

This article analyzes the use of microexpressions – subtle facial movements that are difficult for the human eye to notice, and even more difficult to immediately analyze, even specialists in the field do not always succeed in this perfectly, because their speed is only 1/5 to 1/3 of a second, for assessment of psychological state using artificial intelligence methods. The research is aimed at improving the analysis of micro-mimicry for accurate identification of emotions and psychological state. An overview of implemented technological solutions based on CNN was conducted, and a method for their improvement was found. An experimental test conducted on video recordings of people experiencing various emotions showed the high accuracy of the developed method in recognizing emotions and psychological state. Despite the challenges of the scarcity of microexpression datasets and the subtlety of facial movements, the paper presents a CapsuleNet model for microexpression recognition, builds a system architecture, and conducts testing. By combining three main data sets (SMIC, CASME II and SAMM) into a unified cross-database, the method developed in the work tests the possibility of generalization of the model by different subject characteristics. The performance of CapsuleNet, evaluated by cross-baseline benchmarking and Leave-One-Object-Out validation, significantly outperforms the baseline (LBP-TOP) and other improved of an CNN models. The paper shows that the performance of the developed model, determined by unweighted average recall and F1 scores, outperforms both the LBP-TOP baseline and other state-of-the-art CNN models. In a comprehensive microexpression recognition system. First, we process the data to identify the peak frames in the sequences and isolate the face region in these frames. These processed face images are then moved to CapsuleNet for the classification. The results of the work is to develop and complement methods of emotional artificial intelligence, offering new insights into micromimic assessment of psychological states that affect mental health, human-computer interaction, and social robotics. This technology has potential for development and expansion. This is an additional opportunity for companies that work with people and it is important for them to monitor their productivity, as it is directly related to the psychological state.

Enhancing Li+ recovery in brine mining: integrating next-gen emotional AI and explainable ML to predict adsorption energy in crown ether-based hierarchical nanomaterials.

Artificial intelligence (AI) is being employed in brine mining to enhance the extraction of lithium, vital for the manufacturing of lithium-ion batteries, through improved recovery efficiencies and the reduction of energy consumption. An innovative approach was proposed combining Emotional Neural Networks (ENN) and Random Forest (RF) algorithms to elucidate the adsorption energy (AE) (kcal mol-1) of Li+ ions by utilizing crown ether (CE)-incorporated honeycomb 2D nanomaterials. The screening and feature engineering analysis of honeycomb-patterned 2D materials and individual CE were conducted through Density Functional Theory (DFT) and Gaussian 16 simulations. The selected honeycomb-patterned 2D materials encompass graphene, silicene, and hexagonal boron nitride, while the specific CEs evaluated are 15-crown-5 and 18-crown-6. The crown-passivated 2D surfaces held a significant adsorption site through van der Waals forces for efficient recovery of Li+ ions. ENN predicted the targeted adsorption sites with high precision and minimal deviation. The eTAI (XAI) based Shapley Additive exPlanations (SHAP) was also explored for insight into the feature importance of CE embedded 2D nanomaterials for the recovery of Li+ ions. The extreme gradient boosting algorithm (XGBoost) model demonstrated a RT-2-MAPE = 0.4618% and ENN-2-MAPE = 0.4839% for the feature engineering analysis. This research would be an insight into the AI-driven nanotechnology that presents a viable and sustainable approach for the extraction of natural resources through the application of brine mining.

The Anchoring Effect, Algorithmic Fairness, and the Limits of Information Transparency for Emotion Artificial Intelligence

Emotion artificial intelligence (AI) is shown to vary systematically in its ability to accurately identify emotions, and this variation creates potential biases. In this paper, we conduct an experiment involving three commercially available emotion AI systems and a group of human labelers tasked with identifying emotions from two image data sets. The study focuses on the alignment between facial expressions and the emotion labels assigned by both the AI and humans. Importantly, human labelers are given the AI’s scores and informed about its algorithmic fairness measures. This paper presents several key findings. First, the labelers’ scores are affected by the emotion AI scores, consistent with the anchoring effect. Second, information transparency about the AI’s fairness does not uniformly affect human labeling across different emotions. Moreover, information transparency can even increase human inconsistencies. Plus, significant inconsistencies in the scoring among different emotion AI models cast doubt on their reliability. Overall, the study highlights the limitations of individual decision making and information transparency regarding algorithmic fairness measures in addressing algorithmic fairness. These findings underscore the complexity of integrating emotion AI into practice and emphasize the need for careful policies on emotion AI.