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Human opacity is the intrinsic quality of unknowability of human beings with respect to machines. The descriptive relationship between humans and machines, which captures how much information one can gather about the other, can be explicated using an opacity-transparency relationship. This relationship allows us to describe and normatively evaluate a spectrum of opacity where humans and machines may be either opaque or transparent. In this paper, we argue that the advent of Affective Computing (AC) has begun to shift the ideal position of humans on this spectrum towards greater transparency, while much of this technology is shifting towards opacity. We explore the implications of this shift with regard to the affective information of humans and how the threat to human opacity by AC systems has various adverse repercussions, such as infringement of one's autonomy, deception, manipulation, and increased anxiety. There are also distributive consequences that expose vulnerable groups to unjustified burdens and reduce them to mere profiles. We further provide an assessment of current AC technology, which follows the descriptive relationship between humans and machines from the lens of opacity and transparency. Finally, we foresee and address three possible objections to our claims. These are the beneficence of AC systems, their relation to privacy, and their restrictive capacity to capture human affects. Through these arguments, the paper aims to bring attention to the ontological relationship between humans and machines from the perspective of opacity and transparency while emphasizing on the gravity of the ethical concerns raised by their threat to human opacity. [ABSTRACT FROM AUTHOR]

Several prominent criticisms have recently challenged the possibility of algorithmically determining or recognising human affect. This paper ethically evaluates one underexplored avenue for overcoming such deficiencies in categorical affect recognition technologies (ARTs). Specifically, the emerging literature on “situated affectivity” offers valuable guidance on three fronts. First, it conceptually substantiates existing criticism by stressing the contextual dimensions of human affect. Second, in highlighting the breadth of information required for more accurate ARTs, it suggest that they may necessitate penetrating forms of surveillance the ethical literature has already provided good reason to resist. Third, it corroborates postphenomenological insights concerning the socially co-constituting character of technology; and thereby grounds the need for a “situated ethics” sensitive to the ways that ARTs might reconstruct human affectivity (and reinforce existing power hierarchies). The deep worry is that this concern is likely to persist even if concerns over accuracy and surveillance are mitigated. A detailed consideration of this novel ethical concern suggests that the ethically acceptable use of ARTs may only be possible in highly contained settings. Yet doubts over such containment places advocates of ARTs in a “double bind” they need to address. [ABSTRACT FROM AUTHOR]

Video games are one of the richest and most popular forms of human-computer interaction and, hence, their role is critical for our understanding of human behaviour and affect at a large scale. As artificial intelligence (AI) tools are gradually adopted by the game industry a series of ethical concerns arise. Such concerns, however, have so far not been extensively discussed in a video game context. Motivated by the lack of a comprehensive review on the ethics of AI as applied to games, we survey the current state of the art in this area and discuss ethical considerations of these systems from the holistic perspective of the affective loop. Through the components of this loop, we study the ethical challenges that AI faces in video game development. Elicitation highlights the ethical boundaries of artificially induced emotions; sensing showcases the trade-off between privacy and safe gaming spaces; and detection, as utilised during in-game adaptation, poses challenges to transparency and ownership. This paper calls for an open dialogue and action for the games of today and the virtual spaces of the future. By setting an appropriate framework we aim to protect users and to guide developers towards safer and better experiences for their customers. [ABSTRACT FROM AUTHOR]

In recent years, many agent-based models of human groups have implemented a mechanism of emotion contagion, yet empirical validation is lagging behind. The aim of the present paper is to validate an agent-based model of emotion contagion at the level of group emotion, by comparing simulations against the emotional development of real people in small groups. To study the effect of emotion contagion, the participants interacted via a video call, where they were virtually placed in different social environments while they played a quiz. This allowed the exchange of emotion among all, some or none of the participants. The patterns of emotional development in the empirical results supported our hypotheses based on literature of emotion contagion and social norms. Further, the simulations with the complete model resembled many of these patterns. When emotion contagion was disabled in the model, the resemblance decreased. These results give a first indication that emotion contagion occurs in groups that meet via video calls, and can in-part be predicted by the proposed model of emotion contagion. Yet, further study with a larger and more diverse empirical sample is needed, as well as comparisons across contagion mechanisms, to draw stronger conclusions and ultimately justify societal application. [ABSTRACT FROM AUTHOR]

This work presents a longitudinal study of diversity among the Affective Computing research community members. We explore several dimensions of diversity, including gender, geography, institutional types of affiliations and selected combinations of dimensions. We cover the last 10 years of the IEEE Transactions on Affective Computing (TAFFC) journal and the International Conference on Affective Computing and Intelligent Interaction (ACII), the primary sources of publications in Affective Computing. We also present an analysis of diversity among the members of the Association for the Advancement of Affective Computing (AAAC). Our findings reveal a “leaky pipeline” in the field, with a low –albeit slowly increasing over the years– representation of women. They also show that academic institutions clearly dominate publications, ahead of industry and governmental centres. In terms of geography, most publications come from the USA, contributions from Latin America or Africa being almost non-existent. Lastly, we find that diversity in the characteristics of researchers (gender and geographic location) influences diversity in the topics. To conclude, we analyse initiatives that have been undertaken in other AI-related research communities to foster diversity, and recommend a set of initiatives that could be applied to the Affective Computing field to increase diversity in its different facets. The diversity data collected in this work are publicly available, ensuring strict personal data protection and governance rules. [ABSTRACT FROM AUTHOR]

Thanks to the many submissions we have received, we can present this second part of our special issue on "AI-eXplained." In here, we continue our mission to demystify the intricate world of artificial intelligence and make it accessible to a broader audience. As AI continues to evolve and integrate into various aspects of our lives, it becomes increasingly important to bridge the gap between experts and those eager to understand the inner workings of AI systems. Our goal remains unchanged—to present AI concepts in a comprehensible, engaging, and interactive manner, empowering our readers to explore and grasp the enchanting world of AI. [ABSTRACT FROM AUTHOR]

Stunning advances in machine learning are heralding a new era in sensing, interpreting, simulating and stimulating human emotion. In the human sciences, research is increasingly highlighting the explanatory power of emotions, feelings, and other affective processes to predict how we think and behave. This is beginning to translate into an explosion of applications that can improve human wellbeing including methods to reduce stress and improve emotion regulation skills, techniques to support healthier social media use, pain monitoring in neonates, and decision-support tools that recognize emotional bias. [ABSTRACT FROM AUTHOR]

Welcome to this March 2024 Special Issue on Advancing Sustainable Development Goals with AI This issue features research on how AI and data technologies can be used to advance the United Nations (UN) sustainable development goals (SDGs). [ABSTRACT FROM AUTHOR]

Ethical affective computing (AC) requires maximizing the benefits to users while minimizing its harm to obtain trust from users. This requires responsible development and deployment to ensure fairness, bias mitigation, privacy preservation, and accountability. To obtain this, we require methodologies that can quantify, visualize, analyze, and mine insights from affect data. Hence, in this article, we propose a spatio-temporal model for representing secondary affect data from network sciences’ perspective. We propose a network science-based model to represent spatio-temporal data, e.g., action units’ sequences, and continuous affect reports. In particular, the proposed model captures the spatial and temporal strength of the relationship among essential variables in the data. The proposed model allows to analyze data as a whole system. We also demonstrated the use case of the model for graph analytics on secondary affect data that can assist to measure and quantify several issues that can be originated from the study setup, data recording devices, and the influences/biases that can originate from the perspective of the affect reporters. We also demonstrated the use cases of the proposed method on ethical trustworthy emotional AI via measuring biases from de-identified data and how it contributes towards ethics, transparency, value alignment, and governance. [ABSTRACT FROM AUTHOR]

Recent technological and educational shifts have made it possible to capture students’ facial expressions during learning with the goal of detecting learners’ emotional states. Those interested in affect detection argue these tools will support automated emotions-based learning interventions, providing educational professionals with the opportunity to develop individualized, emotionally responsive instructional offerings at scale. Despite these proposed use-cases, few have considered the inherent ethical concerns related to detecting and reporting on learners’ emotions specifically within applied educational contexts. As such, this article utilizes a Reflexive Principlism approach to establish a typology of proactive reflexive ethical implications in tracking students’ emotions through changes in their facial expressions. Through this approach the authors differentiate between use in research and applied education contexts, arguing that the latter should be curtailed until the ethics of affective computing in educational settings is better established. [ABSTRACT FROM AUTHOR]

As the popularity of wearables increases, so does their utility for studying emotions. Using new technologies points to several ethical challenges to be considered to improve research designs. There are several ethical recommendations for utilizing wearables to study human emotions, but they focus on emotion recognition systems applications rather than research design and implementation. To address this gap, we have developed a perspective on wearables, especially in daily life, adapting the ReCODE Health - Digital Health Framework and companion checklist. Therefore, our framework consists of four domains: (1) participation experience, (2) privacy, (3) data management, and (4) access and usability. We identified 33 primary risks of using wearables to study emotions, including research-related negative emotions, collecting, processing, storing, sharing personal and biological information, commercial technology validity and reliability, and exclusivity issues. We also proposed possible strategies for minimizing risks. We consulted the new ethical guidelines with members of ethics committees and relevant researchers. The judges (N = 26) positively rated our solutions and provided useful feedback that helped us refine our guidance. Finally, we summarized our proposals with a checklist for researchers’ convenience. Our guidelines contribute to future research by providing improved protection of participants’ and scientists’ interests. [ABSTRACT FROM AUTHOR]