Social media (SM) influence young adults’ communication practices. Artificial Intelligence (AI) is increasingly used for making recommendations on SM. Yet, its effects on different generations of SM users are unknown. SM can use AI recommendations to sort texts and prioritize them, shaping users’ online and offline experiences. Current literature primarily addresses technological or human-user perspectives, overlooking cognitive perspectives. This research aims to propose methods for mapping users’ interactions with AI recommendations (AiRS) and analyzes how embodied interactions mediated by a (...) digital agent can lead to changes in social and cultural practices. For this, this work proposes a comparative analysis of central practices evoked by AI recommendations-mediated communication on SM among users in Italy, Estonia, and the Netherlands in the age category 18–26 years old. The data used in the comparative analysis was collected via semi-structured interviews and elaborated based on cognitive psychology and semiotics. This research highlights the contextual significance of AI recommendations as a mediator in creating new communication practices. Findings confirm that young adults often choose practices that would enhance their digital representations according to AiRS’ dominant patterns and categories. AiRS impacts individual interpretations and practices and can further affect social and cultural levels. (shrink)

The use of social media as a recruitment tool for research with humans is increasing, and likely to continue to grow. Despite this, to date there has been no specific regulatory guidance and there has been little in the bioethics literature to guide investigators and institutional review boards faced with navigating the ethical issues such use raises. We begin to fill this gap by first defending a nonexceptionalist methodology for assessing social media recruitment; second, examining respect for privacy (...) and investigator transparency as key norms governing social media recruitment; and, finally, analyzing three relatively novel aspects of social media recruitment: the ethical significance of compliance with website “terms of use”; the ethics of recruiting from the online networks of research participants; and the ethical implications of online communication from and between participants. Two checklists aimed at guiding investigators and IRBs through the ethical issues are included as appendices. (shrink)

In recent years, social networks have made significant progress and the number of people who use them to communicate is increasing day by day. The vast amount of information available on social networks has led to the importance of using friend recommender systems to discover knowledge about future communications. It is challenging to choose the best machine learning approach to address the recommender system issue since there are several strategies with various benefits and drawbacks. In light of this, (...) a solution based on the stacking approach was put out in this study to provide a buddy recommendation system in social networks. Additionally, a decrease in system performance was caused by the large amount of information that was accessible and the inefficiency of some functions. To solve this problem, a particle swarm optimization algorithm to select the most efficient features was used in our proposed method. To learn the model in the objective function of the particle swarm algorithm, a hybrid system based on stacking is proposed. In this method, two random forests and Extreme Gradient Boosting had been used as the base classifiers. The results obtained from these base classifiers were used in the logistic regression algorithm, which has been applied sequentially. The suggested approach was able to effectively address this issue by combining the advantages of the applied strategies. The results of implementation and evaluation of the proposed system show the appropriate efficiency of this method compared with other studied techniques. (shrink)

In the contemporary digital age, recommender systems (RSs) play a fundamental role in managing information on online platforms: from social media to e-commerce, from travels to cultural consumptions, automated recommendations influence the everyday choices of users at an unprecedented scale. RSs are trained on users’ data to make targeted suggestions to individuals according to their expected preference, but their ultimate impact concerns all the multiple stakeholders involved in the recommendation process. Therefore, whilst RSs are useful to reduce information (...) overload, their deployment comes with significant ethical challenges, which are still largely unaddressed because of proprietary constraints and regulatory gaps that limit the effects of standard approaches to explainability and transparency. In this context, I address the ethical and social implications of automated recommendations by proposing a pro-ethical design framework aimed at reorienting the influence of RSs towards societal interest. In particular, after highlighting the problem of explanation for RSs, I discuss the application of beneficent informational nudging to the case of conversational recommender systems (CRSs), which rely on user-system dialogic interactions. Subsequently, through a comparison with standard recommendations, I outline the incentives for platforms and providers in adopting this approach and its benefits for both individual users and society. (shrink)

With the rapid development of the Internet, recommendation systems have received widespread attention as an effective way to solve information overload. Social tagging technology can both reflect users’ interests and describe the characteristics of the items themselves, making group recommendation thus becoming a recommendation technology in urgent demand nowadays. In traditional tag-based recommendation systems, the general processing method is to calculate the similarity and then rank the recommended items according to the similarity. Without considering the (...) influence of continuous user behavior, in this article, we propose a personalized recommendation algorithm based on social tags by combining the ideas of Markov chain and collaborative filtering. This algorithm splits the three-dimensional relationship of into two two-dimensional relationships of and. The user’s interest degree to the tags is calculated by the Markov chain model, and then the items corresponding to them are matched by the recommended tag set. The influence between tags is used to model the satisfaction of items based on the correlation between the tags contained in the matched items, and collaborative filtering is used to complete the sparse values when calculating the interest and satisfaction between user–tags and user–items to improve the accuracy of recommendations. The experiments show that in the publicly available dataset, the personalized recommendation algorithm proposed in this article has significantly improved in accuracy and recall rate compared with the existing algorithms. (shrink)

The Internet and electronic communication technologies have taken the psychological field by storm. From the innovations of new web interventions for easier access to care to the increased ease of client scheduling and communication, these developments have greatly advanced mental health care. However, these advantages are also laced with ethical implications that warrant attention. Without judicious consideration, social media use by psychotherapists can lead to inadvertent self-disclosures to clients that risk damaging the therapeutic alliance, interfering with therapeutic processes, and (...) placing both the client and clinician at risk. A better understanding of the ethical implications of social media use is warranted so that guidelines for appropriate use can be developed and implemented. This article highlights the potential risks associated with social media use by psychotherapists and, in absence of formalized guidelines, offers recommendations for best practices. (shrink)

Given the rapid growth of participatory media content such as blogs, there is a need to design personalized recommender systems to recommend only useful content to users. We be- lieve that in addition to producing useful recommendations, certain insights from media research such as simplification and opinion diversity in recommendations should form the foundations of such recommender systems, so that the be- havior of the systems can be understood more closely, and modified if necessary. We propose and evaluate such a (...) sys- tem based on a Bayesian user-model. We use the underlying social network of blog authors and readers to model the pref- erence features for individual users. The initial results of our proposed solution are encouraging, and set the agenda for fu- ture research. Introduction. (shrink)

The article presents specific recommendations for the examination of AI systems in medicine developed by the authors. The recommendations based on the problems, risks and limitations of the use of AI identified in scientific and philosophical publications of 2019-2022. It is proposed to carry out ethical expertise of projects of medical AI, by analogy with the review of projects of experimental activities in biomedicine; to conduct an ethical review of AI systems at the stage of preparation for their development followed (...) by monitoring the testing of the created system; to focus on bioethical principles during the examination of AI systems for medical purposes. (shrink)

ABSTRACT To date, the American Psychological Association Ethical Standards and Code of Conduct does not include direct guidance about how psychologists should navigate social media. Given the variety of roles psychologists can choose to engage on social media, it is imperative that guidelines are established. These guidelines should consider the multidimensionality that exists as psychologists may choose to present on social media through a personal presence, a business presence, and/or even an influencer/content creator presence. Specific ethical considerations (...) and recommendations are provided. (shrink)

This paper considers current personalized recommendation approaches based on computational social systems and then discusses their advantages and application environments. The most widely used recommendation algorithm, personalized advice based on collaborative filtering, is selected as the primary research focus. Some improvements in its application performance are analyzed. First, for the calculation of user similarity, the introduction of computational social system attributes can help to determine users’ neighbors more accurately. Second, computational social system strategies can be (...) adopted to penalize popular items. Third, the network community, identity, and trust can be combined as there is a close relationship. Therefore, this paper proposes a new method that uses a computational social system, including a trust model based on community relationships, to improve the user similarity calculation accuracy to enhance personalized recommendation. Finally, the improved algorithm in this paper is tested on the online reading website dataset. The experimental results show that the enhanced collaborative filtering algorithm performs better than the traditional algorithm. (shrink)

This study examines the relationship between corporate social responsibility -related information and the value of financial analysts’ stock recommendations. The information environment in which analysts operate in is affected by CSR-related reports that companies voluntarily issue as well as information that becomes available through third-party analysis and rating institutions. We find an inverse relationship between the value of both upgrade and downgrade revisions and the supply of CSR-related information compiled by third-party institutions, suggesting that CSR-related data are associated with (...) a richer information environment that makes it more challenging for analysts to issue informative recommendations, thereby mitigating their contribution to the price discovery process. We further find that the value of analysts’ recommendation revisions is lower for companies that voluntarily issue CSR-related reports compared to those that do not make such disclosures and that the overall effect of CSR on the informativeness of analysts is stronger in the recent years. Our findings have implications for the evolution of CSR reporting as the incorporation of CSR-related information into market prices legitimizes the relevance of such reports. Furthermore, these findings contribute to our understanding of the role and value of information intermediaries in changing information environments. (shrink)

As the pace of modern life accelerates, social exclusion occurs more and more frequently in interpersonal interactions. The type of social exclusion can lead to different psychological needs of individuals, and, thus, affects the tendency of word-of-mouth recommendation. There are three experiments in this research. Experiment 1 explores the influence of social exclusion types on the willingness of WOM recommendation. The result shows that being rejected increases individuals' willingness to WOM recommendations while being ignored decreases (...) individuals' willingness. Experiment 2 explores the internal psychological mechanism of the influence of social exclusion types on WOM recommendation behavior and proves the mediating role of psychological needs. In experiment 3, the moderating effect of product attributes on the main effect is analyzed. This research is the first to explore the influence of social exclusion types on individuals' willingness to WOM recommendations, which enriches the research on social exclusion in the field of WOM recommendations. (shrink)

This article explores four major areas of moral concern regarding virtual reality technologies. First, VR poses potential mental health risks, including Depersonalization/Derealization Disorder. Second, VR technology raises serious concerns related to personal neglect of users’ own actual bodies and real physical environments. Third, VR technologies may be used to record personal data which could be deployed in ways that threaten personal privacy and present a danger related to manipulation of users’ beliefs, emotions, and behaviors. Finally, there are other moral and (...) social risks associated with the way VR blurs the distinction between the real and illusory. These concerns regarding VR naturally raise questions about public policy. The article makes several recommendations for legal regulations of VR that together address each of the above concerns. It is argued that these regulations would not seriously threaten personal liberty but rather would protect and enhance the autonomy of VR consumers. (shrink)

Social work and bioethics are fields deeply committed to cross-disciplinary collaboration to do their respective work. While scholars and practitioners from both fields share a commitment to social justice and to respecting the dignity, integrity and the worth of all persons, the overlap between the fields, including shared values, has received little attention. The purpose of this article is to describe the ways in which greater collaboration between the two fields can broaden their scope, enrich their scholarship, and (...) better ground their practice. We describe the potential for realizing such benefits in two areas – health care ethics consultation and social inequalities in health – arguing that the fields both complement and challenge one another, making them ideal partners for the interdisciplinary inquiry and problem-solving so often called for today in health and health care. (shrink)

Point-of-interest recommendations are a popular form of personalized service in which users share their POI location and related content with their contacts in location-based social networks. The similarity and relatedness between users of the same POI type are frequently used for trajectory retrieval, but most of the existing works rely on the explicit characteristics from all users’ check-in records without considering individual activities. We propose a POI recommendation method that attempts to optimally recommend POI types to serve multiple (...) users. The proposed method aims to predict destination POIs of a user and search for similar users of the same regions of interest, thus optimizing the user acceptance rate for each recommendation. The proposed method also employs the variable-order Markov model to determine the distribution of a user’s POIs based on his or her travel histories in LBSNs. To further enhance the user’s experience, we also apply linear discriminant analysis to cluster the topics related to “Travel” and connect to users with social links or similar interests. The probability of POIs based on users’ historical trip data and interests in the same topics can be calculated. The system then provides a list of the recommended destination POIs ranked by their probabilities. We demonstrate that our work outperforms collaborative-filtering-based and other methods using two real-world datasets from New York City. Experimental results show that the proposed method is better than other models in terms of both accuracy and recall. The proposed POI recommendation algorithms can be deployed in certain online transportation systems and can serve over 100,000 users. (shrink)

There are many studies conducted on recommendation systems, most of which are focused on recommending items to users and vice versa. Nowadays, social networks are complicated due to carrying vast arrays of data about individuals and organizations. In today’s competitive environment, companies face two significant problems: supplying resources and attracting new customers. Even the concept of supply-chain management in a virtual environment is changed. In this article, we propose a new and innovative combination approach to recommend organizational people (...) in social networks based on organizational communication and SCM. The proposed approach uses a hybrid strategy that combines basic collaborative filtering and demographic recommendation systems, using data mining, artificial neural networks, and fuzzy techniques. The results of experiments and evaluations based on a real dataset collected from the LinkedIn social network showed that the hybrid recommendation system has higher accuracy and speed than other essential methods, even substantially has eliminated the fundamental problems with such systems, such as cold start, scalability, diversity, and serendipity. (shrink)

Social isolation and loneliness are growing concerns around the globe that put people at increased risk of disease and early death. One much‐touted approach to addressing them is deploying artificially intelligent agents to serve as companions for socially isolated and lonely people. Focusing on digital humans, we consider evidence and ethical arguments for and against this approach. We set forth and defend public health policies that respond to concerns about replacing humans, establishing inferior relationships, algorithmic bias, distributive justice, and (...) data privacy. (shrink)

In a backlash against the prevalence of statistical methods, recently social scientists have focused more on studying causal mechanisms. They increasingly rely on a technique called process-tracing, which involves contrasting the observable implications of several alternative mechanisms. Problematically, process-tracers do not commit to a fundamental notion of causation, and therefore arguably they cannot discern between mere correlation between the links of their purported mechanisms and genuine causation. In this paper, I argue that committing to Woodward's interventionist notion of causation (...) would solve this problem: process-tracers should take into account evidence for possible interventions on the mechanisms they study. (shrink)

The article brings to light the use of recommender systems as technologies of the self, complementing the observations in current literature regarding their employment as technologies of ‘soft’ power. User practices on the music recommendation website last.fm reveal that many users do not only utilize the website to receive guidance about music products but also to examine and transform an aspect of their self, i.e. their ‘music taste’. The capacity of assisting users in self-cultivation practices, however, is not unique (...) to last.fm but stem from certain properties shared by all recommendation systems. Furthermore, unlike other oft-studied digital/web technologies of the self which facilitate ‘self-publishing’ vis-à-vis virtual companions in social media, recommender algorithms themselves can act as ‘intimate experts’, accompanying users in their self-care practices. Thus, recommendation systems can facilitate both algorithmic control and creative self-transformation, which calls for a theorization of this new cultural medium as a space of tension. (shrink)

Recommender Systems (RecSys) have been around since the early days of the Internet, helping users navigate the vast ocean of information and the increasingly available options that have been available for us ever since. The range of tasks for which one could use a RecSys is expanding as the technical capabilities grow, with the disruption of Machine Learning representing a tipping point in this domain, as in many others. However, the increase of the technical capabilities of AI-powered RecSys did not (...) come with a thorough consideration of their ethical implications and, despite being a well-established technical domain, the potential impacts of RecSys on their users are still under-assessed. This paper aims at filling this gap in regards to one of the main impacts of RecSys: personal autonomy. We first describe how technology can affect human values and a suitable methodology to identify these effects and mitigate potential harms: Value Sensitive Design (VSD). We use VSD to carry out a conceptual investigation of personal autonomy in the context of a generic RecSys and draw on a nuanced account of procedural autonomy to focus on two components: competence and authenticity. We provide the results of our inquiry as a value hierarchy and apply it to the design of a speculative RecSys as an example. (shrink)

This short piece draws on political philosophy to show how social media interference operations can be used by hostile states to weaken the apparent legitimacy of democratic governments. Democratic societies are particularly vulnerable to this form of attack because democratic governments depend for their legitimacy on citizens' trust in one another. But when citizen see one another as complicit in the distribution of deceptive content, they lose confidence in the epistemic preconditions for democracy. The piece concludes with policy recommendations (...) for how democratic governments should protect themselves. (shrink)

Research Group in Global Health: Ethics, Culture and Economics, Department of Public Health and Primary Care, University of Bergen, Kalfarveien 31, 5018The WHO Commission on Social Determinants of Health has documented pervasive inequalities in health in many countries. These are clearly associated with unfair distribution of the social determinants of health. Policies directed at reducing this unfair distribution should be promoted across all sectors and institutions responsible for securing equal opportunities and freedom for all citizens. This article argues (...) that such policies will need to balance the competing goals of reducing social group inequalities in health and improving average health. We need a public debate about how to make such difficult choices. This paper outlines a framework that can help us structure our thinking about this dilemma. CiteULike Connotea Del.icio.us What's this? (shrink)

This report analyses the relationship between RRI-like practices and competitive advantage. RRI frameworks have traditionally been less oriented towards their application in competitive environments; hence resulting in limitations to the applicability of some of its main tenets in industry and in the context of the development of a national competitive advantage. Aiming to close this gap and identify how a competitive advantage based on engagement in RRI-like practices across world regions may be developed, a systematic literature review, a survey and (...) case studies were carried out. Five main drivers of competitive advantage through RRI-like practices were identified: avoiding uncompetitive regulation, increasing social acceptance, incorporating stakeholder needs and tapping into new markets, increasing the efficiency of the innovation process, and reputational effects. On the other hand, four barriers were identified: obstacles during the research and innovation process, protecting intellectual property, lack of consumer awareness, and barriers derived from the institutional environment. The survey revealed that, while there are some differences in terms of attitudes and engagement in RRI-like practices across regions, both procedural and outcome dimensions were relevant. However, the application of particular practices in exercising such dimensions showed more variations across regions, reflecting adaption to local environments. In relation to competitive advantage, outcome dimensions and open and transparent innovation processes showed a clear relationship with performance, in particular with customer performance. The reason for this might lie in the increased visibility of such practices to the consumer. Two cases studies were carried out focusing on the management of socio-ethical concerns through RRI-like practices and their relationship with competitive advantage. The case on the bio-economy domain, identified different responses depending on local regulations and the focus placed on the development of competitive advantages at the micro and macro levels, and showcased the importance of domain specific considerations in RRI-like responses. The ICT case highlighted the importance of network approaches and second-order reflexivity, and the need to adapt RRI-like practices to local contexts to maximise their benefits for competitive advantage. Lastly, the analysis of the two cases concentrating on transversal issues made notable how strategic approaches to RRI and their proper integration in strategy showed an improved relation with competitive advantage. This study makes a significant contribution to existing research on RRI-like practices and competitive advantage and adds to the literature on business involvement in RRI that has been flourishing despite the tradition of overlooking RRI by actors in competitive environments. Moreover, it provides a set of practical recommendations for industry, policymakers, research performing organizations, research funding organizations, investors, civil society and NGOs and association bodies. These recommendations are oriented towards developing and sustaining a competitive advantage based on RRI-like practices by research and innovation actors, while supported by other stakeholders in the system. The advice is informed by the research study and proposes the need to tailor and adopt bottom-up approaches in the implementation of RRI-practices, integrating RRI-like logics and competitive advantage logics into organizational dynamic, and the need for collaboration among different actors, apart from recommendations particular to each stakeholder. (shrink)

Current methods to capture, analyse and present the audience participation of broadcast events are increasingly carried out using social media. Uptake of such technology tools has so far been poor amongst older adults, and it has the worrying effect of excluding the demographic from participation. Our work explores whether a common desire to interact with debates can be tapped with technology with a very low barrier to entry, to both support better engagement with broadcast debates and encourage greater use (...) of social media. This paper describes experiments where older adults interact with a BBC radio debate programme: The Moral Maze. As a result, we obtained common interaction patterns which then are used to define recommendations for software-supported interaction with debates based on theories of argumentation. Our goal is to combine research on computational models of argument and user-driven research on human-centred computing in a project with the potential for high-profile impact in addressing older adults inclusion in the digital economy. (shrink)

For the last forty years, two claims have been at the core of disputes about scientific change: that scientists reason rationally and that science is progressive. For most of this time discussions were polarized between philosophers, who defended traditional Enlightenment ideas about rationality and progress, and sociologists, who espoused relativism and constructivism. Recently, creative new ideas going beyond the polarized positions have come from the history of science, feminist criticism of science, psychology of science, and anthropology of science. Addressing the (...) traditional arguments as well as building on these new ideas, Miriam Solomon constructs a new epistemology of science. After discussions of the nature of empirical success and its relation to truth, Solomon offers a new, social account of scientific rationality. She shows that the pursuit of empirical success and truth can be consistent with both dissent and consensus, and that the distinction between dissent and consensus is of little epistemic significance. In building this social epistemology of science, she shows that scientific communities are not merely the locus of distributed expert knowledge and a resource for criticism but also the site of distributed decision making. Throughout, she illustrates her ideas with case studies from late-nineteenth- and twentieth-century physical and life sciences. Replacing the traditional focus on methods and heuristics to be applied by individual scientists, Solomon emphasizes science funding, administration, and policy. One of her goals is to have a positive influence on scientific decision making through practical social recommendations. (shrink)

Digital media has become an integral part of people’s lives, and its ubiquity and pervasiveness in our everyday lives raise new ethical, social, cultural, political, economic and legal issues. Many of these issues have primarily been dealt with in terms of what is ‘right’ or ‘just’ with digital media and digitally-mediated practices, and questions about the relations between digital media and the good life are often left in the background. In short, what is often missing is an explicit discussion (...) of the relations between digital media and the good life. Under the label of ‘Net recommendation’, the present study aims to offer a balanced and constructive normative analysis of digital media, focusing on the relations between digital media and the good life. The project of Net recommendation aims to (re)assert the importance of actual discourses in our normative analysis of the relations between digital media and the good life. I pursue this project with a Walzerian approach to digital media and the good life that takes seriously (and, ideally, also interacts with) actual discourses. This approach allows us to have a better understanding of our normative judgements on the impacts of digital media has (or will have) on the good life and, at the same time, allows us to answer the question of ‘how should we live with digital media?’ more adequately. Moreover, the Walzerian approach is useful in both an intra-cultural and an inter¬cultural context. Using China’s Internet as a case study, I show that the relations between digital media and the good life in each culture should be examined in its own right. Together, I hope, this study provides an adequate illustration and defence of the project of Net recommendation that does not start with the assumption of digital media being a source of moral problem and strives to recommending specific ways to reform and/or transform digital media and digitally-mediated practices that allow us to have better relations with digital media and enable us to live better lives with them. (shrink)

This paper examines folk theories of algorithmic recommendations on Spotify in order to make visible the cultural specificities of data assemblages in the global South. The study was conducted in Costa Rica and draws on triangulated data from 30 interviews, 4 focus groups with 22 users, and the study of “rich pictures” made by individuals to graphically represent their understanding of algorithmic recommendations. We found two main folk theories: one that personifies Spotify and another one that envisions it as a (...) system full of resources. Whereas the first theory emphasizes local conceptions of social relations to make sense of algorithms, the second one stresses the role of algorithms in providing a global experience of music and technology. We analyze why people espouse either one of these theories and how these theories provide users with resources to enact different modalities of power and resistance in relation to recommendation algorithms. We argue that folk theories thus offer a productive way to broaden understanding of what agency means in relation to algorithms. (shrink)

How social media impacts the autonomy of its users is a topic of increasing focus. However, much of the literature that explores these impacts fails to engage in depth with the philosophical literature on autonomy. This has resulted in a failure to consider the full range of impacts that social media might have on autonomy. A deeper consideration of these impacts is thus needed, given the importance of both autonomy as a moral concept and social media as (...) a feature of contemporary life. By drawing on this philosophical literature, we argue that autonomy is broadly a matter of developing autonomy competencies, having authentic ends and control over key aspects of your own life, and not being manipulated, coerced, and controlled by others. We show how the autonomy of users of social media can be disrespected and harmed through the control that social media can have over its users’ data, attention, and behaviour. We conclude by discussing various recommendations to better regulate social media. (shrink)

In this article we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change and it contribute to combating the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and (...) the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI’s greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combating climate change, while reducing its impact on the environment. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Kennedy Institute of Ethics Journal 14.1 (2004) 47-54 [Access article in PDF] Ethical Guidelines for Human Embryonic Stem Cell Research*(A Recommended Manuscript) Adopted on 16 October 2001Revised on 20 August 2002 Ethics Committee of the Chinese National Human Genome Center at Shanghai, Shanghai 201203 Human embryonic stem cell (ES) research is a great project in the frontier of biomedical science for the twenty-first century. Be- cause the research involves (...) the use of human embryos, it triggers serious debate on ethical issues. Opponents consider the embryo to be an early form of human life that should be respected and not destroyed. However, the majority of scientists support embryonic stem cell research, believing that it offers good prospects for the treatment of diseases that have remained incurable until now and so will benefit humankind. The Ethics Committee of the Department of Ethical, Legal, and Social Implications of the Chinese National Human Genome Center at Shanghai seriously discussed the ethical debate initiated by embryonic stem cell research. We concluded that we should support the scientists of our country in actively carrying out human embryonic stem cell research for the noble cause of "medicine being a beneficent art." For the healthy and orderly development of human embryonic stem cell research in our country, we put forward the following recommended Ethical Guidelines for Human Embryonic Stem Cell Research as a reference for leaders, administrative departments, and related scientists. Preface Article 1. Human embryonic stem cells are the primitive cells that play the main role in the growth and development of a human body. These [End Page 47] primitive cells have the potential for infinite proliferation, self-renewal, and multi-directional differentiation. If scientists can discover the mechanism of differentiation of human embryonic stem cells, it will be possible to induce differentiation of human embryonic stem cells to form various types of human cells for clinical cell therapy. If human embryonic stem cell research can be integrated with modern biomedical engineering techniques, it also will be possible to make repair and replacement of human tissues and organs a reality. Article 2. There are two ways to classify human embryonic stem cells. One way is to classify them according to their potential for differentiation. There could be three kinds of stem cells: totipotent stem cells, pluripotent stem cells, and unipotent stem cells.A totipotent stem cell has the potential to develop into a whole individual. It can differentiate into the more than 200 cell types in the whole body, construct any tissue or organ of the body, and finally develop into a whole individual. The fertilized egg and the cleavage cells at the very early stage of embryonic development are totipotent stem cells.A pluripotent stem cell has the potential to differentiate into many cell types derived from the three embryonic layers. However, it has lost the capacity to develop into a complete organism.A unipotent stem cell is derived from the further differentiation of the pluripotent stem cell. It can differentiate only into one cell type such as a hematopoietic stem cell, neural stem cell, and others.The other way is to classify human stem cells according to their source. There could be two kinds of human stem cells: embryonic stem cells and tissue stem cells (also called adult stem cells). The former involves experimentation with embryos, which has serious ethical implications. Ethical issues associated with the latter are mainly expressed in the various opinions regarding the allocation of health resources. Article 3. Human embryonic stem cells are the group of cells called the blastocyst inner cell mass during the early stage of embryonic development. They are the main source of totipotent stem cells, and hence the focal and hot point in stem cell research. Studies on the clinical application of embryonic stem cells probably will involve use of the somatic cell nucleus transfer (SCNT) technique, which destroys the early human embryo. At present, the ethical and moral debate is very serious in human embryonic stem cell research regarding whether the research will develop... (shrink)

This article analyses the ethical aspects of multistakeholder recommendation systems (RSs). Following the most common approach in the literature, we assume a consequentialist framework to introduce the main concepts of multistakeholder recommendation. We then consider three research questions: who are the stakeholders in a RS? How are their interests taken into account when formulating a recommendation? And, what is the scientific paradigm underlying RSs? Our main finding is that multistakeholder RSs (MRSs) are designed and theorised, methodologically, according (...) to neoclassical welfare economics. We consider and reply to some methodological objections to MRSs on this basis, concluding that the multistakeholder approach offers the resources to understand the normative social dimension of RSs. (shrink)

The paper deals with the state of the social sciences after the boom of internet services in the Czech Republic in the 1990s. The results of our survey, based on 512 responses from the economics and history departments of major Czech public universities, show that internet services are considered a quality factor for academic output; however, the issues of plagiarism, a lack of resource criticism, inadequacy of impact factor-based evaluations, poor academic training for the new generation of social (...) scientists, the failure of state academic policy, and the generation gap make further development in the Czech social sciences rather problematic. As a result we recommend creating a better communication link between policy makers and scholars, reforming the current state policy which encourages lower quality academic output, and improving academic training, which requires a more individual approach, and also placing higher demands on social scientists. (shrink)

Algorithmic decision making is used in an increasing number of fields. Letting automated processes take decisions raises the question of their accountability. In the field of computational journalism, the algorithmic accountability framework proposed by Diakopoulos formalizes this challenge by considering algorithms as objects of human creation, with the goal of revealing the intent embedded into their implementation. A consequence of this definition is that ensuring accountability essentially boils down to a transparency question: given the appropriate reverse-engineering tools, it should be (...) feasible to extract design criteria and to identify intentional biases. General limitations of this transparency ideal have been discussed by Ananny and Crawford :973–989, 2018). We further focus on its technical limitations. We show that even if reverse-engineering concludes that the criteria embedded into an algorithm correspond to its publicized intent, it may be that adversarial behaviors make the algorithm deviate from its expected operation. We illustrate this issue with an automated news recommendation system, and show how the classification algorithms used in such systems can be fooled with hard-to-notice modifications of the articles to classify. We therefore suggest that robustness against adversarial behaviors should be taken into account in the definition of algorithmic accountability, to better capture the risks inherent to algorithmic decision making. We finally discuss the various challenges that this new technical limitation raises for journalism practice. (shrink)

Emergency situations require individuals to make important changes in their behavior. In the case of the COVID-19 pandemic, official recommendations to avoid the spread of the virus include costly behaviors such as self-quarantining or drastically diminishing social contacts. Compliance (or lack thereof) with these recommendations is a controversial and divisive topic, and lay hypotheses abound regarding what underlies this divide. This paper investigates which cognitive, moral, and emotional traits separate people who comply with official recommendations from those who don't. (...) In four studies (three pre-registered) on both U.S. and French samples, we found that individuals' self-reported compliance with official recommendations during the COVID-19 pandemic was partly driven by individual differences in moral values, disgust sensitivity, and psychological reactance. We discuss the limitations of our studies and suggest possible applications in the context of health communication. (shrink)

In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, (...) and the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI’s greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based, and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combatting climate change, while reducing its impact on the environment. (shrink)

Martha Nussbaum has powerfully argued in Frontiers ofJustice and elsewhere that John Rawls’s sort of social-contract theory cannot usefully be deployed to deal with issues pertaining to justice for the disabled. To counter this claim, this article deploys Rawls’s sort of social-contract theory in order to deal with issues pertaining to justice for the disabled—or, since, as Nussbaum stresses, we all have some degree of disability—for the severely disabled. In this way, rather than questioning one by one Nussbaum’s (...) interpretive claims about Rawls’s view, one can simply see how the Rawlsian framework can work in application to this issue. Following Rawls’s lead, the paper utilizes the idealized “initial choice situation” as an analytic and comparative device for examining alternative principles of justice, developing three different interpretations of the initial choice situation that each correspond to a different set of principles that apply to people of all levels of disability. One of these sets of principles is a simple extension of Rawls’s, one is very close to what Nussbaum herself recommends, and the third is a kind of hybrid. In this way, it is shown not only that Rawls’s social-contract device can usefully be applied to these issues, but also that it is helpful for exploring the deep commitments underlying each of these competing sets of principles. (shrink)

This article considers the social construction of mental toughness in line with prevailing social attitudes towards success and dominance in elite sport. Critical attention is drawn to the research literature which has sought to conceptualise mental toughness and the idealistic rhetoric and metaphor with which it has done so. The concept of mental toughness currently reflects an elitist ideal, constructed along the lines of the romantic narrative of the ‘Hollywood hero’ athlete. In contrast, the mental and moral virtues (...) which should form the basis of mental toughness are often neglected when an athlete ‘fails’. Currently, mental toughness exists as a characteristic used to describe successful athletes and is only applied in hindsight. Finally, we recommend that the morally problematic association of mental toughness (within the media, society, and the research community) with ultimate success needs to be removed in order to rescue the concept from the elitist discourses which currently surround and suffocate it. (shrink)

Big data and Machine learning Techniques are reshaping the way in which food safety risk assessment is conducted. The ongoing ‘datafication’ of food safety risk assessment activities and the progressive deployment of probabilistic models in their practices requires a discussion on the advantages and disadvantages of these advances. In particular, the low level of trust in EU food safety risk assessment framework highlighted in 2019 by an EU-funded survey could be exacerbated by novel methods of analysis. The variety of processed (...) data raises unique questions regarding the interplay of multiple regulatory systems alongside food safety legislation. Provisions aiming to preserve the confidentiality of data and protect personal information are juxtaposed to norms prescribing the public disclosure of scientific information. This research is intended to provide guidance for data governance and data ownership issues that unfold from the ongoing transformation of the technical and legal domains of food safety risk assessment. Following the reconstruction of technological advances in data collection and analysis and the description of recent amendments to food safety legislation, emerging concerns are discussed in light of the individual, collective and social implications of the deployment of cutting-edge Big Data collection and analysis techniques. Then, a set of principle-based recommendations is proposed by adapting high-level principles enshrined in institutional documents about Artificial Intelligence to the realm of food safety risk assessment. The proposed set of recommendations adopts Safety, Accountability, Fairness, Explainability, Transparency as core principles (SAFETY), whereas Privacy and data protection are used as a meta-principle. (shrink)

With the deployment of information and communication technologies and the needs of data and information sharing within cities, smart city aims to provide value-added services to improve citizens’ quality of life. But, currently city planners/developers are faced with inadequate contextual information on the dimensions of smart city required to achieve a sustainable society. Therefore, in achieving sustainable society, there is need for stakeholders to make strategic decisions on how to implement smart city initiatives. Besides, it is required to specify the (...) smart city dimensions to be adopted in making cities smarter for sustainability attainment. But, only a few methods such as big data, internet of things, cloud computing, etc. have been employed to support smart city attainment. Thus, this study integrates case-based reasoning as an artificial intelligence technique to develop a recommender system towards promoting smart city planning. CBR provides suggestions on smart city dimensions to be adopted by city planners/decision-makers in making cities smarter and sustainable. Accordingly, survey data were collected from 115 respondents to evaluate the applicability of the implemented CBR recommender system in relation to how the system provides best practice recommendations and retaining of smart city initiatives. Results from descriptive and exploratory factor analyses suggest that the developed system is applicable in supporting smart city adoption. Besides, findings from this study are expected to provide valuable insights for practitioners to develop more practical strategies and for researchers to better understand smart city dimensions. (shrink)

In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, (...) and the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI’s greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based, and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combatting climate change, while reducing its impact on the environment. (shrink)

The framework of the ‘social ontology of the internet’ is applied to music recommendation platforms. Those websites provide individual suggestions of music to users, creating new dynamics of taste that are no longer based on human-to-human interaction and verbalized judgments. An exemplary analysis of three platforms shows that different conceptions of musical tastes are represented by technical systems: situational emotional preferences, a formalist aesthetics, and social proximity based on tastes. The platforms share certain assumptions about the ontology (...) of musical entities and of course the constitutive act of recommending. We discuss how this act can be ascribed to technical systems. Theses on the platforms’ effects on the social structure of musical tastes are developed. (shrink)