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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]

Ethical understanding aims at morally analyzing and discriminating ethical scenarios described in natural language. By classifying behaviors that occur in ethical scenarios as ethical or unethical, ethical understanding empowers artificial intelligence systems to understand human values so as to discern right from wrong morally. However, most existing ethical understanding methods lack fine-grained analysis and cannot handle the problem that an ethical scenario may contain multiple behaviors with multiple polarities. In this paper, we introduce a novel natural language processing task, behavior-based ethical understanding (BEU), for mining the purpose relation(s) and ethical polarity of a specific behavior from the social news. It contains three subtasks: behavior term extraction (BTE) to extracts behavior terms, purpose relation inference (PRI) to identifies purposive relations among behaviors, and polarity discrimination (PD) to predicts the ethical polarities of behaviors, respectively. To perform this task, we constructed a Chinese BEU dataset, named FG-ETHICS. Besides, we propose a three-stage framework, BEU-BERT, based on the pre-trained language model BERT and deliberately designed downstream models for three subtasks. Experimental results show that the proposed framework achieves the best performance from the BTE and PD tasks, and achieves a promising performance of 75% on the PRI task. [ABSTRACT FROM AUTHOR]

After 20 years of testing a framework for affective user responses to artificial agents and robots, we compiled a full formalization of our findings so to make the agent respond affectively to its user. Silicon Coppélia as we dubbed our system works from the features of the observed other, appraises these in various domains (e.g., ethics and affordances), then compares them to goals and concerns of the agent, to finally reach a response that includes intentions to work with the user as well as a level of being engaged with the user. This ultimately results in an action that adds to or changes the situation both agencies are in. Unlike many other systems, Silicon Coppélia can deal with ambiguous emotions of its user and has ambiguous ‘feelings’ of its own, which makes its decisions quite human-like. In the current paper, we advance a fuzzy-sets approach and show the inner workings of our system through an elaborate example. We present a number of simulation experiments, one of which showed decision behaviors based on biases when agent goals had low priorities. Silicon Coppélia is open to scrutiny and experimentation by way of an open-source implementation in Ptolemy. [ABSTRACT FROM AUTHOR]

This article is an extension of a work in progress paper originally presented at the conference FiE 2020, Frontiers in Education by (Schirichian et al., 2020), as a result of Schirichian’s master’s in science dissertation. It studies the relations between academic performance, protection factors, life at university, and personal characteristics to understand how they influence students’ academic success in undergraduate engineering programs in Brazil. In this challenging environment, several students overcome these issues and graduate, which indicates that some protection structures allow students to face challenging situations and be successful in their studies. Current research studied the relationships between academic performance, self-efficacy, causal attributions, personal characteristics, and life at university, with a group of 30 students. Models were studied by combining the different variables and aspects evaluated in the research, and the findings show relationship between academic performance and: 1) self-efficacy, causal attributions, and personal characteristics (such as leisure activities, living with their parents, or college accommodations) and those who attended private schools during basic education; 2 protection factors self-efficacy and causal attributions with advancing in the program and the time of enrolment in the university; and 3) the dimensions initiation and persistence, success/internality and failure/internality, indicating that those who perceive themselves as playing a major role in their academic journey tend to have better outcomes. Further results show the influence of social inequalities, particularly for those students who declared themselves brown and who conducted their basic education in public schools (which are generally of lower quality than private ones in Brazil). [ABSTRACT FROM AUTHOR]

In this paper we provide a proof of principle of a new method for addressing the ethics of autonomous vehicles (AVs), the Data-Theories Method, in which vehicle crash data is combined with philosophical ethical theory to provide a guide to action for AV algorithm design. We use this method to model three scenarios in which an AV is exposed to risk on the road, and determine possible actions for the AV. We then examine how different philosophical perspectives on agent partiality, or the degree to which one can act in one’s own self-interest, might address each scenario. This method shows why modelling the ethics of AVs using data is essential. First, AVs may sometimes have options that human drivers do not, and designing AVs to mimic the most ethical human driver would not ensure that they do the right thing. Second, while ethical theories can often disagree about what should be done, disagreement can be reduced and compromises found with a more complete understanding of the AV’s choices and their consequences. Finally, framing problems around thought experiments may elicit preferences that are divergent with what individuals might prefer once they are provided with information about the real risks for a scenario. Our method provides a principled and empirical approach to productively address these problems and offers guidance on AV algorithm design. [ABSTRACT FROM AUTHOR]

Adopting speech as their mode of interaction, speech-enabled conversational agent (CA) systems hold the potential to enable more natural and engaging self-report experiences than traditional media (e.g., pen and paper, web, or smartphone systems). Our recent research concerns the potential design and use of CAs to support mental health and wellbeing. In this article, we present findings from a study during which 22 individuals with affective disorders (e.g., depression, bipolar disorder) used either a speech-enabled CA or web app to self-report their emotional wellbeing. Analysis of users' experiences and engagement with the system for daily self-report suggests that despite many technical limitations, users rated self-reporting via CA as a significantly novel and attractive experience yielding similar rates of engagement to a traditional web-based method. [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]

Ethical issues remain a significant concern for future large-scale deployment of autonomous vehicles. Although machine ethics have been developed for a few decades, ethical decision making in autonomous driving imposes more complex and emerging challenges to which some dedicated efforts from academia, policymakers, and automakers have been devoted in the past few years. This paper reviews the efforts and progress associated with the various aspects of ethical challenges in autonomous vehicles. Based on the critical review, it is clear that a consistent solution for ethical challenges does not currently exist and recommendations are provided, to potentially encourage the involving players to collaborate to tackle the remaining challenges. [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]

Argument mining is defined as the task of automatically identifying and extracting argumentative components (e.g., premises, claims, etc.) and detecting the existing relations among them (i.e., support, attack, rephrase, no relation). One of the main issues when approaching this problem is the lack of data, and the size of the publicly available corpora. In this work, we use the recently annotated US2016 debate corpus. US2016 is the largest existing argument annotated corpus, which allows exploring the benefits of the most recent advances in natural language processing in a complex domain like argument (relation) mining. We present an exhaustive analysis of the behavior of transformer-based models (i.e., BERT, XLNET, RoBERTa, DistilBERT, and ALBERT) when predicting argument relations. Finally, we evaluate the models in five different domains, with the objective of finding the less domain-dependent model. We obtain a macro F1-score of 0.70 with the US2016 evaluation corpus, and a macro F1-score of 0.61 with the Moral Maze cross-domain corpus. [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]

The application of artificial intelligence (AI) in healthcare has gained tremendous popularity over recent years, given its enormous potential for strengthening the quality and efficiency of healthcare services on a global scale. However, the deployment of AI in healthcare comes with both benefits and risks for patients, healthcare professionals, and the whole society. Ethical concerns, such as how to prevent AI from perpetuating preexisting health disparities while incorporating its full potential in a traditional medical setting, have been pointed out by many researchers and policymakers ,. As regulatory frameworks and guidelines concerning ethical issues have been published by many organizations, there is a pressing need to present an overview of existing documents to see what has been achieved and what has yet to be emphasized in the field of AI health-related applications. [ABSTRACT FROM AUTHOR]

Recent work in Natural Language Processing has increasingly focused on detecting suicidal intent in textual data, where the main aim is to detect expressions in a binary setting. However, previous research has shown that search results and other mentions of suicide online are not only limited to expressions of suicidal intent. Therefore, previously proposed algorithms and datasets might for example struggle to distinguish between suicidal intent of a user and suicide mentioned in a humorous context. In this article we introduce a new dataset called TWISCO, which proposes an alternative approach to classifying expressions of suicidality online. For this, we use a coding framework developed in Psychology to distinguish between different mentions of suicide. Next, we present a variety of machine and deep learning baselines in three different classification settings (text only, features only and text and features). Furthermore, we introduce a Feature GCN that improves performance over the GCN baseline. Finally, we investigate the hypothesis that feelings of dominance are correlated with users expressing their own suicidality. We provide an in-depth discussion of the trade-offs in classifying suicidal intent online. [ABSTRACT FROM AUTHOR]

Microtransactions, small in-app purchases within virtual environments, have become increasingly prevalent in modern gaming, often leading to addictive behaviors and financial burdens, particularly among young male players. The potential consequences of such microtransactions within the metaverse extend beyond gaming, as they provide companies with unprecedented control over user experiences and the ability to capitalize personal data for targeted advertising and manipulative practices. This article presents a comprehensive framework for the ethical analysis and design of microtransactions in the metaverse to address these concerns. This three-dimensional framework explores Motivation, Engagement, and Fairness to evaluate the impact of microtransactions on user autonomy, social interactions, and overall fairness. Additionally, the paper conducts a running example on World of Warcraft to assess the alignment of its microtransaction design choices with the proposed ethical principles. By shedding light on these issues, this article aims to raise awareness among consumers, developers, and policymakers about the need for responsible practices, consumer protection, and privacy preservation in the metaverse. It emphasizes fostering collaboration and transparency to safeguard users from misuse while promoting a user-centric metaverse ecosystem.

Although the ethics of cybersecurity might seem to be simple, the matter can be surprisingly complicated. This paper discusses the results of an extensive study aimed at uncovering the anticipated, emerging ethical issues related to cybersecurity. First, it discusses the “strong signals”, i.e., the “mainstream” worries and concerns. Then, it uncovers the “weak signals” - the hidden, less-discussed concerns, which may still define the upcoming future of the ethics of cybersecurity. The results of the study are also compared to the outcomes of a similar experiment conducted two years ago, in order to see if the upcoming ethical dilemmas anticipated back then have in fact become a reality.

There is concern about robots taking people’s jobs. Advances in automation risk bringing material and psychological harm to workers, so it is important to study the ethics of this engineering discipline. Existing work focuses on either policy to ameliorate workers’ income loss or the effects of losing agency. What is missing is a guide to help automation engineers and roboticists evaluate their moral responsibilities. This article addresses that gap by providing some motivation for these practitioners to consider the ethics of job obsolescence and tools to evaluate relevant professional choices. [ABSTRACT FROM AUTHOR]

The language employed by an individual when discussing topics of a moral nature (of the kind typically found in, e.g., social media) is revealing not only of the text affective contents itself, but also of the individual who wrote the text in the first place. Based on these observations, this work intends to illustrate how two kinds of morality-related information may be inferred from text by presenting a number of shallow and deep learning models of moral stance and moral foundations classification. In doing so, we introduce a novel corpus of texts labelled with moral foundation scores, and a novel approach to fine-grained, human-centric moral foundations classification that is, to the best of our knowledge, among the first NLP studies of this kind. [ABSTRACT FROM AUTHOR]

Transcranial direct current stimulation (tDCS) has become a new method of rehabilitation treatment for stroke and is gradually gaining acceptance. However, the neurophysiological mechanisms of tDCS in the treatment of stroke still need to be further explored. In our research, we aimed to investigate the effects of tDCS on neural oscillation power and brain functional network (BFN) connectivity in stroke patients based on electroencephalogram (EEG). Fifteen patients with ischemic stroke attended two experimental sessions in a randomized crossover trial and received real and sham tDCS. Resting-state EEG signals were acquired before and after stimulation. First, the power of EEG delta, theta, alpha, beta, and gamma bands were analyzed before and after stimulation. Then, BFNs in different bands were constructed, and network characteristic parameters such as degree, clustering coefficient, characteristic path length, global efficiency, and small-world attribute were analyzed. The results showed that the delta power decreased significantly (${P}< {0.05}$) and the alpha power increased significantly (${P}< {0.05}$) after real tDCS, whereas the theta, beta, and gamma power did not change significantly (${P}>{0.05}$). The degree and global efficiency of the delta band BFN decreased significantly after real tDCS (${P}< {0.05}$), the characteristic path length increased significantly (${P}< {0.05}$), and the clustering coefficient and small-world attribute decreased but not statistically significant (${P}>{0.05}$). The degree, clustering coefficient, global efficiency, and small-world attribute of the alpha band BFN increased significantly after real tDCS (${P}< {0.05}$), and the characteristic path length decreased significantly (${P}< {0.05}$). The BFN characteristic parameters in theta, beta, and gamma bands did not change significantly after real tDCS (${P}>{0.05}$). There was no significant difference in sham tDCS group (${P}>{0.05}$). Our study found that the power of delta oscillations decreased and the global connectivity of delta band BFN weakened after tDCS in stroke patients, whereas the power of alpha oscillations increased and the global and local connectivity of alpha band BFN was enhanced. The findings implied that one of the neurophysiological mechanisms of tDCS treatment in stroke patients may be the modulation of the power of delta and alpha oscillations, as well as the improvement of the connectivity of delta and alpha oscillation networks. [ABSTRACT FROM AUTHOR]

Transforming into an agile organization is challenging because it involves complex changes across the organization including changes to strategy, structure, culture, operations, and technology. Although much has been written about organizational agility, practitioners still call for authentic accounts and concrete experiences to help them understand how to transform. This article is based on three case studies of diverse organizations transforming to agility and provides an account of each transformation. Each organization chose a different focus for their transformation: culture change, strategic change, and operational change. Each organization faced challenges that resulted in tensions. We present 13 tensions from three cases, which illustrate the kinds of tension that organizations face in agile transformations. We consider these 13 tensions through the lens of paradox theory and thereby produce both practical guidance and theoretical contributions. We show how questions generated from this perspective may be used to guide transformation leaders and managers in addressing the tensions they will meet. Our findings provide empirical evidence for the tension categories in paradox theory and show that tensions occur during transformations to agility and not just in fully agile organizations. [ABSTRACT FROM AUTHOR]

In construction companies, employee burnout rates and negative socioemotional behaviors (NSEBs) are severe problems for the management. Work-related problems are then visible in the employee's psychological behaviors and consequent outcomes. The literature on organizational behavior argues that moral leadership can evade burnout and NSEB. Following the arguments, this article attempts to examine the impact of moral leadership on the employee's burnout and NSEB through the mediating variables of job embeddedness, and distributive justice in the construction industry. By using the social exchange theory, this article analyzes the data of 302 employees from construction companies in Hong Kong to assess these relationships. The results showed a negative relationship between moral leadership and the construction employees’ burnout. This article provides construction companies and their managers with useful insights on the way the moral leadership mediate through job embeddedness and distributive justice to curtail the employees' burnout. The results of this article also motivate scholars to investigate the multidimensional role of moral leadership in their future research. [ABSTRACT FROM AUTHOR]

Contribution: This article measures the impact of a service-learning project in a first-year introduction to computing course on students’ understanding of engineers’ roles and responsibilities and their attitudes toward plastic pollution. Background: Service learning has been integrated into classrooms for many years, but its utilization in engineering courses is lacking. Furthermore, plastic pollution is a complex and multidisciplinary problem that requires social, political, environmental, and engineering solutions. This work provides a quantitative assessment on whether service learning through a litter pickup project impacts students’ views of the role of engineers in solving complex sociotechnical problems. Research Questions: 1) How does service learning change students’ perception of an engineer’s role in solving sociotechnical problems? and 2) how can service learning be used to increase students’ environmental awareness and action? Methodology: Surveys were administered to students before and after their participation in a service-learning project. The study was conducted across five sections of an introduction to a computer programming course that is required of first-year civil, electrical, and mechanical engineering students. Findings: After the service-learning project, students were more likely to agree that engineers have the skills, knowledge, and responsibility to solve societal problems. Students also demonstrated a more holistic understanding of plastic pollution and reported changes in their waste reduction behaviors. [ABSTRACT FROM AUTHOR]

Background: Self-disclosure on social media can cause some privacy risks, but it benefits users and businesses if adequately managed. Companies may benefit from users’ self-disclosure on social media to better understand the consumers’ needs, customize services or products, and address users’ concerns to sculpt positive brand reputation, trust, and sales. In addition, users’ better understanding of self-disclosure motivations helps them manage more suitable topic, platform, and concepts to match the intended online personal or professional persona. Finally, technical communicators’ understanding of the motivation for social media self-disclosures can help them leverage available self-disclosure in producing more effective technical communication and carefully plan self-disclosures with clear motivations. Literature review: Many researchers have studied the varying self-disclosure motivations, but to the best of our knowledge, no mapping studies are currently available summarizing the motivations of self-disclosure on social media. Research question: What does the current research about self-disclosure identify as users’ motivation for self-disclosure on social media? Research methodology: We conducted a systematic mapping study that included relevant journal and conference publications. Mapping studies are suitable for structuring a broad research field concerning research questions about content, methods, or trends in the existing publications. Results: We found four categories for social media self-disclosures: discloser-related, audience-related, platform- and affordances-related, and perceived risk- and cost-related. Within the main categories, we found varying submotivations. We also discuss the implications of our findings and future research needs. Conclusions: The mapping of available studies helps researchers, academics, and practitioners understand existing self-disclosure motivations and research gaps. In addition, social media stakeholders planning to use social media self-disclosures within their areas of interest can use this study as a starting point to understand what drives social media self-disclosures. [ABSTRACT FROM AUTHOR]

Background: This article reports on survey-based research of technical and professional communication (TPC) teachers and administrators, illustrating how these participants implement social justice and intercultural communication pedagogies in service learning (SL). Literature review: We situate this research in relation to existing scholarship about SL in TPC, SL and social justice, and SL as it intersects with intercultural communication. Research question: How do technical and professional communication teachers and administrators across the US infuse their SL pedagogies with social justice and intercultural communication theories in practice? Research methodology: Using purposive sampling, we surveyed 55 TPC teachers and administrators about their experiences with and attitudes toward social justice and intercultural communication in SL. Results/discussion: We identify what courses are reported as sites of SL projects as well as participants’ self-reported perceptions about social justice in SL. In addition, we outline four themes related to the application of social justice and intercultural communication theories to SL: activities, constraints, points of resistance, and goals and outcomes. Conclusion: We conclude with recommendations for TPC administrators and programs, and by briefly discussing implications for TPC practitioners and future directions for research. [ABSTRACT FROM AUTHOR]

Trust is necessary for any kind of complex social organization. Whether trust is the result of past exchanges with a specific individual (e.g., direct reciprocity) or the result of norms and conventions within a social network (e.g., indirect reciprocity), understanding the basis for trust is a prerequisite for successful coordination between agents. Nowhere is this more evident than in the healthcare setting. Traditionally, exchanges in healthcare occur between healthcare professionals (HCPs, e.g., doctors, nurses) and patients or parents. [ABSTRACT FROM AUTHOR]

In recent years, there has been a steady increase in awareness of the need to design technology more ethically. The crashes of two Boeing 737 MAX airplanes as well as the Volkswagen scandal have contributed to the questioning of ethical practices in classic engineering departments. Several studies revealed the potential bias and manipulability of software systems, including critical judicial systems and social networks. The lack of artificial intelligence (AI) transparency became subject to a stream of criticism. To the positive surprise of long-term scholars in the social issues of technology, the IT industry woke up to the need for a more forward-looking, responsible, and ethical planning of IT systems. [ABSTRACT FROM AUTHOR]

Fifty-two years ago, Milton Friedman proclaimed that the “Social Responsibility of Business is to Increase its Profits.” The corporate executive, he said, owed one thing to one group: profit to his “employers,” the shareholders of the firm. And with this rallying cry, he summoned forth a near 30-year movement (involving scholars, regulators, political parties, and others) to force the American corporation to conform to his vision, by doing everything from increasing shareholder control of the firm to linking CEO compensation with short-term share prices. [ABSTRACT FROM AUTHOR]

This article proposes a synthesis of a novel dynamic incentive mechanism and optimal control for moral hazard in power grids with real-time power balancing markets. For the liberalization of the balancing markets, it is an indispensable requirement to integrate dynamic subsystems controlled by individual agents into a stabilized power grid via an incentive mechanism. Meanwhile, without the proper use of the agents’ private information, high-speed market-clearing achieving an appropriate social objective cannot be ensured. Consequently, a market mechanism is needed that contains some dynamic incentive mechanisms for eliciting their private information and proper controls under moral hazard, which is called incentivizing market in this article. Drawing on dynamic contract theory in microeconomics, a novel method for designing the incentivizing market is proposed on the basis of the integration of economic models and a dynamic grid model. Conventional contract problems have been analyzed for both static and dynamic systems whose control inputs are operated directly by an independent system operator. However, these analyses do not accord with the design for the dynamic incentive mechanism in the incentivizing market. The approach in this article requires adapting the dynamic contract problems to the market. To achieve this objective, the fundamental formulas for optimal design are established and the basic properties of the designed market based on a model-based approach are clarified. We next propose optimal pricing and control synthesis for social-objective designs. The performance of our proposed method is illustrated through simulations with an Institute of Electrical Engineers of Japan (IEEJ) East 30-machine system. [ABSTRACT FROM AUTHOR]

Rapid technological developments in the last decade have contributed to using machine learning (ML) in various economic sectors. Financial institutions have embraced technology and have applied ML algorithms in trading, portfolio management, and investment advising. Large-scale automation capabilities and cost savings make the ML algorithms attractive for personal and corporate finance applications. Using ML applications in finance raises ethical issues that need to be carefully examined. We engage a group of experts in finance and ethics to evaluate the relationship between ethical principles of finance and ML. The paper compares the experts’ findings with the results obtained using natural language processing (NLP) transformer models, given their ability to capture the semantic text similarity. The results reveal that the finance principles of integrity and fairness have the most significant relationships with ML ethics. The study includes a use case with SHapley Additive exPlanations (SHAP) and Microsoft Responsible AI Widgets explainability tools for error analysis and visualization of ML models. It analyzes credit card approval data and demonstrates that the explainability tools can address ethical issues in fintech, and improve transparency, thereby increasing the overall trustworthiness of ML models. The results show that both humans and machines could err in approving credit card requests despite using their best judgment based on the available information. Hence, human-machine collaboration could contribute to improved decision-making in finance. We propose a conceptual framework for addressing ethical challenges in fintech such as bias, discrimination, differential pricing, conflict of interest, and data protection.

Surgical robots have been widely adopted with over 4000 robots being used in practice daily. However, these are telerobots that are fully controlled by skilled human surgeons. Introducing “surgeon-assist”—some forms of autonomy—has the potential to reduce tedium and increase consistency, analogous to driver-assist functions for lane-keeping, cruise control, and parking. This article examines the scientific and technical backgrounds of robotic autonomy in surgery and some ethical, social, and legal implications. We describe several autonomous surgical tasks that have been automated in laboratory settings, and research concepts and trends. [ABSTRACT FROM AUTHOR]

In August 2020, students took to the streets of London in a mass protest over their A-level results (that determine entry to university), chanting “F*ck the algorithm.” Their A-level grades had been determined by an algorithm that depended on past school performance, a system of teacher ratings, and for smaller student groups, the use of Centre Assessed Grades. Commendably Ofqual (the U.K. Government Office of Qualifications and Examinations Regulation) were transparent in their use of the algorithm so that affected stakeholders were informed of the algorithm’s existence and the key details of how it was developed. The design of the data set produced an algorithm with biased outputs, which hardcoded preexisting educational, policy, and societal biases. Due to the large numbers of students, the single “results day” output and the wider interests of schools, teachers, parents, and universities, the unfairness of the outcome was magnified. Add to this that the students were, by definition, academically proficient individuals, many of whom derived from privileged and supportive backgrounds, which meant that they had the awareness and capacity to challenge. Hence, the impact of this biased algorithm was not received silently and so it was hard to deny, deflect, and dismiss. However, this is not always the case, and many systems are deployed with similar biases and harmful outcomes but the impact is dissipated over time and individuals, and therefore not so immediately obvious and easy to challenge. This is particularly the case when the people impacted do not have the advantages that enabled the A-level students to challenge. [ABSTRACT FROM AUTHOR]

The first international Workshop on Artificial Intelligence for Equity (AI4Eq) was held virtually in Santiago de Compostela, Spain, in conjunction with ECAI2020. The focus of the workshop was to examine the role of artificial intelligence (AI) science and technology in advancing toward the United Nations (UN) Sustainable Development Goals, particularly in the context of a mix of standards, policies, regulations, declarations, guidelines, and charters on the ethics of AI, and “trustworthy” and “responsible” development of AI. A particular theme emerged identifying pressing issues of human rights, personal safety, and modern slavery. [ABSTRACT FROM AUTHOR]

Crowd motion data is fundamental for understanding and simulating realistic crowd behaviours. Such data is usually collected through controlled experiments to ensure that both desired individual interactions and collective behaviours can be observed. It is however scarce, due to ethical concerns and logistical difficulties involved in its gathering, and only covers a few typical crowd scenarios. In this work, we propose and evaluate a novel Virtual Reality based approach lifting the limitations of real-world experiments for the acquisition of crowd motion data. Our approach immerses a single user in virtual scenarios where he/she successively acts each crowd member. By recording the past trajectories and body movements of the user, and displaying them on virtual characters, the user progressively builds the overall crowd behaviour by him/herself. We validate the feasibility of our approach by replicating three real experiments, and compare both the resulting emergent phenomena and the individual interactions to existing real datasets. Our results suggest that realistic collective behaviours can naturally emerge from virtual crowd data generated using our approach, even though the variety in behaviours is lower than in real situations. These results provide valuable insights to the building of virtual crowd experiences, and reveal key directions for further improvements. [ABSTRACT FROM AUTHOR]

The fantasy series The Dynamicist Trilogy (TDT) examines moral and scientific questions concerning the desire to change the world. Readers are challenged to consider whether there is a more ubiquitous notion than changing the world in the news or on the minds of people today. We are concerned with climate change, overpopulation, disease, economics, social justice, information, and misinformation, and many people are talking about affecting these things to transform our world for the better. [ABSTRACT FROM AUTHOR]

Introduction: Community engagement offers one pathway for technical and professional communication (TPC) to enact social justice; however, the community impact of partnerships has often been neglected in favor of more prevalent research on student benefits. This case study examines a community partnership that has emphasized community impact toward distributive justice, sharing its approach to equitable collaboration. About the case: The partnership under study was formed between a university grant writing program and a small nonprofit serving the surviving loved ones of homicide victims. Collaboration within the partnership takes many forms, including service-learning courses in grant writing and content strategy, student internships, and counsel through the organization's board of directors. Situating the case: Research on community engagement has demonstrated its impact on student growth, but community outcomes have not been as robustly investigated or prioritized in partnerships. This case adds to the literature by discussing the collaboration tactics used in a community partnership to achieve community results. Methods: The study uses quantitative outcomes and qualitative feedback from community partners to assess community impact. Partners’ insights about collaboration are put into context with student and faculty processes used in the partnership. Results: Analysis suggests that the partnership has had a meaningful impact on the nonprofit's work in the community, in the form of material resources and sustainable infrastructure. Organization staff identify collaboration tactics that prioritize equity: balancing perspectives, aligning goals, and “showing up.” Conclusion: Further research is needed to broaden the understanding of the connections between collaborative tactics and community impact. [ABSTRACT FROM AUTHOR]

Ethics in cybersecurity is traditionally considered in terms of computer and information ethics and, more recently, cyber warfare –. Although these remain the dominant paradigms there has been growing dissatisfaction with the adequacy of the warfare metaphor in capturing the social and ethical features of arrangementes of cybersecurity and as a means to inform design ,. The result has been an increased focus on a distinct ethics of cybersecurity ,. Complimenting ethical analyses, social cognitive factors that affect users must also be taken into account. For instance, security issues are based on the concepts of identity, social relationships, and the networks that define them (e.g.,). [ABSTRACT FROM AUTHOR]

Contributions: The Chinese University of Hong Kong (CUHK)-Jockey Club AI for the Future Project (AI4Future) co-created the first pretertiary AI curriculum at the secondary school level for Hong Kong and evaluated its efficacy. This study added to the AI education community by introducing a new AI curriculum framework. The preposttest multifactors evaluation about students’ perceptions of AI learning confirmed that the curriculum is effective in promoting AI learning. The teachers also confirmed the co-creation process enhanced their capacity to implement AI education. Background: AI4Future is a cross-sector project that engages five major partners—CUHK’s Faculty of Engineering and Faculty of Education, secondary schools, Hong Kong government, and AI industry. A team of 14 professors collaborated with 17 principals and teachers from six secondary schools to co-create the curriculum. Research Questions: Would the curriculum significantly improve the student perceived competence, attitude, and motivation toward AI learning? How does the co-creation process benefit the implementation of the curriculum? Methodology: The participants were 335 students and eight teachers from the secondary schools. This study adopted a mix-method with quantitative data measures at pre- and post-questionnaires and qualitative data emphasizes teachers’ perspectives on the co-creation process. Paired ${t}$ -tests and ANCOVAs, and thematic analysis were used to analyze the data. Findings: 1) students perceived greater competence and developed a more positive attitude to learn AI and 2) the co-creation process enhanced teachers’ knowledge in AI, as well as fostered teachers’ autonomy in bringing the subject matter into their classrooms. [ABSTRACT FROM AUTHOR]

This roundtable explores the ethical and safety implications of the rapidly evolving technology of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Here, The Task undertaken is to consider and illustrate whether soft law can be an effective and credible means by which to ensure that artificial intelligence (AI) and its uses are unbiased and nondiscriminatory ,. [ABSTRACT FROM AUTHOR]

Construction projects are riddled with risks that should be shared by both owners and contractors alike. The principal-agent theory has proven to be an influential theoretical apparatus in designing optimal risk-sharing rules and provides contractors with proper incentives to improve project efficiencies. According to the principal-agent theory, it is optimal to let the contractor assume all of the risk if it is risk-neutral. However, this article argues that this theoretical model may lead to a biased incentive structure. To demonstrate this point, this article analyzes the necessity of risk-sharing between the owner and risk-neutral contractor in construction projects. Taking into account cases where the respective planning efforts of the owner and contractor are supposed to be unverifiable and mutually complementary in terms of cost overrun risk, we find that either double moral hazard (i.e., both parties take opportunistic strategies) or inefficient breach of contract by the contractor occurs if the contractor assumes the entire cost overrun risk. An optimal risk-sharing strategy, therefore, plays an important role in coordinating both parties to choose efficient strategies. [ABSTRACT FROM AUTHOR]