This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system.

The paper offers a semantic and pragmatic analysis of statements of the form ‘x is beautiful’ as involving a double speech act: first, a report that x is beautiful relative to the speaker’s aesthetic standard, along the lines of naive contextualism; second, the speaker’s recommendation that her audience comes to share her appraisal of x as beautiful. We suggest that attributions of beauty tend to convey such a recommendation due to the role that aesthetic practices play in fostering and enhancing (...) interpersonal coordination. Aesthetic practices are driven by a disposition towards the attunement of attitudes and aesthetic recommendations contribute to forwarding such attunement. Our view is motivated by an attempt to satisfy the following set of desiderata: to account for the experiential nature of aesthetic judgments, disagreements in aesthetic debates, and the normative aspirations of aesthetic discourse, as well as to avoid appealing to error theory and realist ontological commitments. (shrink)

Philosophers have talked to each other about moral issues concerning technology, but few of them have talked about issues of technology and the good life, and even fewer have talked about technology and the good life with the public in the form of recommendation. In effect, recommendations for various technologies are often left to technologists and gurus. Given the potential benefits of informing the public on their impacts on the good life, however, this is a curious state of affairs. (...) In the present paper, I will examine why philosophers are seemingly reluctant to offer recommendations to the public. While there are many reasons for philosophers to refrain from offering recommendations, I shall focus on a specific normative reason. More specifically, it appears that, according to a particular definition, offering recommendations can be viewed as paternalistic, and therefore is prima facie wrong to do so. I will provide an argument to show that the worry about paternalism is unfounded, because a form of paternalism engendered by technology is inevitable. Given the inevitability of paternalism, I note that philosophers should accept the duty to offer recommendations to the public. I will then briefly turn to design ethics, which has reconceptualised the role of philosophers and, in my mind, fitted well with the inevitability of paternalism. Finally, I shall argue that design ethics has to be supplemented by the practice of recommendation if it is to sustain its objective. (shrink)

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)

Advancements in novel neurotechnologies, such as brain computer interfaces and neuromodulatory devices such as deep brain stimulators, will have profound implications for society and human rights. While these technologies are improving the diagnosis and treatment of mental and neurological diseases, they can also alter individual agency and estrange those using neurotechnologies from their sense of self, challenging basic notions of what it means to be human. As an international coalition of interdisciplinary scholars and practitioners, we examine these challenges and make (...) recommendations to mitigate negative consequences that could arise from the unregulated development or application of novel neurotechnologies. We explore potential ethical challenges in four key areas: identity and agency, privacy, bias, and enhancement. To address them, we propose democratic and inclusive summits to establish globally-coordinated ethical and societal guidelines for neurotechnology development and application, new measures, including “Neurorights,” for data privacy, security, and consent to empower neurotechnology users’ control over their data, new methods of identifying and preventing bias, and the adoption of public guidelines for safe and equitable distribution of neurotechnological devices. (shrink)

On March 11, 2020 the World Health Organization classified COVID‐19, caused by Sars‐CoV‐2, as a pandemic. Although not much was known about the new virus, the first outbreaks in China and Italy showed that potentially a large number of people worldwide could fall critically ill in a short period of time. A shortage of ventilators and intensive care resources was expected in many countries, leading to concerns about restrictions of medical care and preventable deaths. In order to be prepared for (...) this challenging situation, national triage guidance has been developed or adapted from former influenza pandemic guidelines in an increasing number of countries over the past few months. In this article, we provide a comparative analysis of triage recommendations from selected national and international professional societies, including Australia/New Zealand, Belgium, Canada, Germany, Great Britain, Italy, Pakistan, South Africa, Switzerland, the United States, and the International Society of Critical Care Medicine. We describe areas of consensus, including the importance of prognosis, patient will, transparency of the decision‐making process, and psychosocial support for staff, as well as the role of justice and benefit maximization as core principles. We then probe areas of disagreement, such as the role of survival versus outcome, long‐term versus short‐term prognosis, the use of age and comorbidities as triage criteria, priority groups and potential tiebreakers such as ‘lottery’ or ‘first come, first served’. Having explored a number of tensions in current guidance, we conclude with a suggestion for framework conditions that are clear, consistent and implementable. This analysis is intended to advance the ongoing debate regarding the fair allocation of limited resources and may be relevant for future policy‐making. (shrink)

This paper recommends how authors of statistical studies can communicate to general audiences fully, clearly, and comfortably. The studies may use statistical methods to explore issues in science, engineering, and society or they may address issues in statistics specifically. In either case, readers without explicit statistical training should have no problem understanding the issues, the methods, or the results at a non-technical level. The arguments for those results should be clear, logical, and persuasive. This paper also provides advice for editors (...) of general journals on selecting high quality statistical articles without the need for exceptional work or expense. Finally, readers are also advised to watch out for some common errors or misuses of statistics that can be detected without a technical statistical background. (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)

In this paper, a personalized online education platform based on a collaborative filtering algorithm is designed by applying the recommendation algorithm in the recommendation system to the online education platform using a cross-platform compatible HTML5 and high-performance framework hybrid programming approach. The server-side development adopts a mature B/S architecture and the popular development model, while the mobile terminal uses HTML5 and framework to implement the function of recommending personalized courses for users using collaborative filtering and recommendation algorithms. By improving the (...) traditional recommendation algorithm based on collaborative filtering, the course recommendation results are more in line with users' interests, which greatly improves the accuracy and efficiency of the recommendation. On this basis, online teaching on this platform is divided into two modes: one mode is the original teacher uploads recorded teaching videos and students can learn by purchasing online or offline download; the other mode is interactive online live teaching. Each course is a separate online classroom; the teacher will publish online class information in advance, and students can purchase to get classroom number and password information online. (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)

As a result of simultaneous developments, including the proliferation of opportunities for online feedback, the application of discourse ethics to journalism, and a greater emphasis on journalistic accountability, the time is ripe for revisiting opportunities that online mechanisms provide for holding journalists accountable to audiences. This paper proposes recommendations to guide hosting online comments in light of the Habermasian framework of discourse ethics developed by Glasser and Ettema (2008). It also explores the limits of such approaches in nations with (...) different press models, such as the development model as practiced in China. (shrink)

It is not unusual for patients and their families, when confronted with difficult medical choices, to ask their physicians for advice. This paper outlines the shades of meaning of two questions frequently put to physicians: “What should I do?” and “What would you do?” It is argued that these are not questions about objective matters of fact. Hence, any response to such questions requires an understanding, appreciation, and disclosure of the personal context and values that inform the recommendation. A framework (...) for considering and articulating a response to these questions is suggested, using as a heuristic the phrasing “If I were you…/If it were me…” Journal of Medical Ethics. (shrink)

Recommender systems assist users in receiving preferred or relevant services and information. Using such technology could be instrumental in addressing the lack of relevance digital mental health apps have to the user, a leading cause of low engagement. However, the use of recommender systems for digital mental health apps, particularly those driven by personal data and artificial intelligence, presents a range of ethical considerations. This paper focuses on considerations particular to the juncture of recommender systems and digital mental health technologies. (...) While separate bodies of work have focused on these two areas, to our knowledge, the intersection presented in this paper has not yet been examined. This paper identifies and discusses a set of advantages and ethical concerns related to incorporating recommender systems into the digital mental health ecosystem. Advantages of incorporating recommender systems into DMH apps are identified as a reduction in choice overload, improvement to the digital therapeutic alliance, and increased access to personal data & self-management. Ethical challenges identified are lack of explainability, complexities pertaining to the privacy/personalization trade-off and recommendation quality, and the control of app usage history data. These novel considerations will provide a greater understanding of how DMH apps can effectively and ethically implement recommender systems. (shrink)

Research on children raises major ethical issues, the most important being the inability of the subjects to provide freely given informed consent. Committees in Canada and the United States charged with formulating recommendations for the protection of human subjects in research came to some fundamentally different conclusions. Two of these are discussed as they apply to children: first the distinction between therapeutic and non-therapeutic research, recognized implicitly by the National Commission and deliberately avoided by the Medical Research Council Working (...) Group ; and second, the looser relation between local committees and Council as defined by guidelines in Canada and the tighter relation between the institutional review committees and the Department of Health, Education and Welfare defined by regulations in the United States. (shrink)

In the July 2001 issue of the JournalofBiosocialScience Tina Moffat presents an interesting study from Nepal (Moffat, 2001). She refers to several studies, among them our study from Nepal’s neighbouring country Bhutan (Bøhler & Ingstad, 1996), to show that weaning practices are determined by different aspects of the environment, and thus cannot be made universal. However, she goes on to conclude that the recommendation of exclusive breast-feeding for 6 months may not be appropriate for the population she studied. This part (...) of her conclusion is, in my view, far from sufficiently supported by her data. (shrink)

This paper explores and rehabilitates the value of decisional privacy as a conceptual tool, complementary to informational privacy, for critiquing personalized choice architectures employed by self-tracking technologies. Self-tracking technologies are promoted and used as a means to self-improvement. Based on large aggregates of personal data and the data of other users, self-tracking technologies offer personalized feedback that nudges the user into behavioral change. The real-time personalization of choice architectures requires continuous surveillance and is a very powerful technology, recently coined as (...) “hypernudging.” While users celebrate the increased personalization of their coaching devices, “hypernudging” technologies raise concerns about manipulation. This paper addresses that intuition by claiming that decisional privacy is at stake. It thus counters the trend to solely focus on informational privacy when evaluating information and communication technologies. It proposes that decisional privacy and informational privacy are often part of a mutually reinforcing dynamic. Hypernudging is used as a key example to illustrate that the two dimensions should not be treated separately. Hypernudging self-tracking technologies compromise autonomy because they violate informational and decisional privacy. In order to effectively judge whether technologies that use hypernudges empower users, we need both privacy dimensions as conceptual tools. (shrink)

This paper proposes a personalized music recommendation method based on multidimensional time-series analysis, which can improve the effect of music recommendation by using user’s midterm behavior reasonably. This method uses the theme model to express each song as the probability of belonging to several hidden themes, then models the user’s behavior as multidimensional time series, and analyzes the series so as to better predict the use of music users’ behavior preference and give reasonable recommendations. Then, a music recommendation method (...) is proposed, which integrates the long-term, medium-term, and real-time behaviors of users and considers the dynamic adjustment of the influence weight of the three behaviors so as to further improve the effect of music recommendation by adopting the advanced long short time memory technology. Through the implementation of the prototype system, the feasibility of the proposed method is preliminarily verified. (shrink)

As the multiple issues of the digital public sphere threaten our democracies and the cohesion of our societies, most attempts for a betterment of the digital networks and platforms revolve around a risk-response approach. This paper takes the opposite approach and develops a positive definition of the ideal ethical public sphere, combining normative features with original taxonomies. In view of defining common standards for a healthy digital public sphere, this paper offers an interdisciplinary literature review, and original recommendations, before (...) discussing potential leverages for implementing these changes. The suggested ethical normative features derive from a positive approach to the digital public sphere, as ideal common standards. Sustained by the underlying assumption that the composite landscape of the media ecosystem and its different layers (networks, platforms, users) cannot be steered by government regulations alone, the normative (deontological) approach serves to understand and justify the need for a normative approach, to help envision new pathways for an ethical transformation. (shrink)

Patients rely on, benefit from, and are strongly influenced by physicians’ recommendations. In spite of the centrality and importance of physicians’ recommendations to clinical care, there is only a scant literature describing the conceptual process of forming a clinical recommendation, and no discrete professional standards for making individual clinical recommendations. Evidence-based medicine and shared decision making together are intended to improve medical decision making, but there has been limited attention to how a recommendation is discretely formulated from (...) either of those processes or how patients’ preferences ought to be considered and how much weight they should hold. Moreover, physicians’ bias has been reported to strongly influence how a recommendation is derived, thereby undermining the quality of healthcare decisions and patients’ trust. To demonstrate a potential for improving the quality of decisions, this article proposes a conceptual framework for how physicians should reach a clinical recommendation and apply the process in practice. For preference-sensitive clinical decisions—that is, clinical decisions when patients’ values and preferences are relevant—the process for reaching a recommendation should be transparent to patients and should be based solely on the medical evidence and patients’ values and preferences. When patients’ preferences for care do not prioritize health, physicians decide whether their recommendation will prioritize a welfare-enhancing versus an autonomy-enhancing approach. When there are gaps in understanding how physicians derive their clinical recommendations and how to further improve the quality of the decisions, the author calls for further empiric research. (shrink)

Recommending the identity of bidders in public procurement auctions has a significant impact in many areas of public procurement, but it has not yet been studied in depth. A bidders recommender would be a very beneficial tool because a supplier can search appropriate tenders and, vice versa, a public procurement agency can discover automatically unknown companies which are suitable for its tender. This paper develops a pioneering algorithm to recommend potential bidders using a machine learning method, particularly a random forest (...) classifier. The bidders recommender is described theoretically, so it can be implemented or adapted to any particular situation. It has been successfully validated with a case study: an actual Spanish tender dataset which has 102,087 tenders from 2014 to 2020 and a company dataset which has 1,353,213 Spanish companies. Quantitative, graphical, and statistical descriptions of both datasets are presented. The results of the case study were satisfactory: the winning bidding company is within the recommended companies group, from 24% to 38% of the tenders, according to different test conditions and scenarios. (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)

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)

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)

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)

Health Recommender Systems are promising Articial-Intelligence-based tools endowing healthy lifestyles and therapy adherence in healthcare and medicine. Among the most supported areas, it is worth mentioning active aging. However, current HRS supporting AA raise ethical challenges that still need to be properly formalized and explored. This study proposes to rethink HRS for AA through an autonomy-based ethical analysis. In particular, a brief overview of the HRS’ technical aspects allows us to shed light on the ethical risks and challenges they might (...) raise on individuals’ well-being as they age. Moreover, the study proposes a categorization, understanding, and possible preventive/mitigation actions for the elicited risks and challenges through rethinking the AI ethics core principle of autonomy. Finally, elaborating on autonomy-related ethical theories, the paper proposes an autonomy-based ethical framework and how it can foster the development of autonomy-enabling HRS for AA. (shrink)

Gamification is increasingly being proposed as a strategy to increase engagement for mental health and wellbeing technologies. However, its implementation has been criticized as atheoretical, particularly in relation to behavior change theory and game studies theories. Definitions of the term “gamification” vary, sometimes widely, between and within academic fields and the effectiveness of gamification is yet to be empirically established. Despite this, enthusiasm for developing gamified mental health technologies, such as interventions, continues to grow. There is a need to examine (...) how best to implement gamification in mental health and wellbeing technologies in a way that takes quick production cycles into account while still emphasizing empirical investigation and building a rigorous evidence base. With reference to game studies and the medical literature, this article interrogates gamification for mental health and wellbeing by examining core properties of the game form. It then explores how gamification can best be conceptualized and implemented for mental health and wellbeing goals from conceptualization through to iterative co-development and evaluation that accommodates software development schedules. Finally, it summarizes its conceptual analysis into recommendations for researchers and designers looking to do so. These recommendations are: assess suitability, implement to support, assess acceptability, evaluate impact, and document comprehensively. These recommendations aim to encourage clear language, unified terminology, the application and evaluation of theory, comprehensive and constant documentation, and transparent evaluation of outcomes. (shrink)

As one of the traditional entertainment items, video background music has gradually changed from traditional consumption to network consumption, which naturally also has the problem of information overload. From the perspective of model design and auxiliary information, this paper proposes a tightly coupled fusion model based on deep learning and collaborative filtering to alleviate the problem of poor prediction accuracy due to sparse matrix in the scoring prediction problem. In the use of auxiliary information, this paper uses crawler technology to (...) obtain auxiliary information on the user side and the video background music side and compensates for the model’s sensitivity to the sparsity of the score matrix from a data perspective. In terms of model design, this paper conducts auxiliary information mining based on the diversity and structural differences of auxiliary information, uses an improved stack autoencoder to learn user’s interests, and uses convolutional neural networks to mine hidden features of video background music. Based on the idea of probabilistic matrix decomposition, the tightly coupled fusion of multiple deep learning models and collaborative filtering is realized. By comprehensively considering user’s interest and video background music characteristics, the collaborative filtering process is supervised, and the optimized prediction result is finally obtained. The performance test and function test of the system were carried out, respectively, to verify the effectiveness of the hybrid recommendation algorithm and the effect of the system for recommendation. Through experimental analysis, it is proved that the algorithm designed in this paper can improve the recommendation quality and achieve the expected goal. (shrink)

Academic publications of empirical public health research often entail recommendations for moral action that address practitioners and policy makers. These recommendations are regularly based on implicit moral judgments with the underlying reasons not explicitly stated. In this paper, we elaborate on the moral relevance of such judgments and the need to explain them in order to account for academic argumentation. We argue for an explicit reporting of bridge principles to increase the transparency of the reporting of public health (...) research. The reporting of bridge principles can inform readers, support them in understanding the relationship between empirical and normative claims in a specific paper, and may pave new ways for the rigorous reporting of empirical research that has moral implications. Furthermore, it can be used to classify studies to systematically address the justification for their argumentation. (shrink)

With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation system, (...) the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered. (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)

More and more tourists are sharing their travel feelings and posting their real experiences on the Internet, generating tourism big data. Online travel reviews can fully reflect tourists’ emotions, and mining and analyzing them can provide insight into the value of them. In order to analyze the potential value of online travel reviews by using big data technology and machine learning technology, this paper proposes an improved support vector machine algorithm based on travel consumer sentiment analysis and builds an Hadoop (...) Distributed File System system based on Map-Reduce model. Firstly, Internet travel reviews are pre-processed for sentiment analysis of the review text. Secondly, an improved SVM algorithm is proposed based on the main features of linear classification and kernel functions, so as to improve the accuracy of sentiment word classification. Then, HDFS data nodes are deployed on the basis of Hadoop platform with the actual tourism application context. And based on the Map-Reduce programming model, the map function and reduce function are designed and implemented, which greatly improves the possibility of parallel processing and reduces the time consumption at the same time. Finally, an improved SVM algorithm is implemented under the built Hadoop platform. The test results show that online travel reviews can be an important data source for travel big data recommendation, and the proposed method can quickly and accurately achieve travel sentiment classification. (shrink)

This article consists of a report submitted to the Massachusetts Commissioner of Mental Health in 1981 from the members of a citizens task force convened for the purpose of recommending policy to the Department in matters concerning privacy and human sexuality. The Chairman of the Committee was given responsibility for producing the information and resources necessary for the members to debate the issue with some intelligence, of facilitating the process and the deliberations, and of writing the final recommendations and (...) report contained in this article.The need for policy in the areas of privacy and human sexuality for persons living in our state institutions serving the handicapped is a continuing and critical one. Resolution of the issues requires informed, courageous decision making by those who are responsible for the welfare of persons with mental or developmental deficiencies. The policy suggested in this article may serve as a step forward for those decision makers. (shrink)

This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by (...) other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society. (shrink)

Neuroscience research has improved our understanding of the long term consequences of sports-related concussion, but ethical issues related to the prevention and management of concussion are an underdeveloped area of inquiry. This article exposes several examples of conflicts of interest that have arisen and been tolerated in the management of concussion in sport (particularly professional football codes) regarding the use of computerized neuropsychological (NP) tests for diagnosing concussion. Part 1 outlines how the recommendations of a series of global protocols (...) for dealing with sports-related concussions (the 1st, 2nd and 3rd Consensus Statements on Concussion in Sport) have endorsed the use of NP testing. The development of these protocols has involved experts who have links with companies that sell computerised NP tests for concussion management. Part 2 describes how some professional football leagues—in particular the National Football League (NFL), the Australian Football League (AFL) and the National Rugby League (NRL)—have mandated specific NP testing products. They have done so on the basis of these international guidelines and by engaging experts who have conflicts of interest with NP testing companies. These decisions have also been taken despite evidence that casts doubt on the reliability and validity of NP tests when used in these ways. (shrink)

In his recent article ‘Nudging, Informed Consent and Bullshit’, William Simkulet1 convincingly argues that certain types of nudging satisfy Frankfurt’s criteria of bullshit. As a prelude to this argument, Simkulet considers whether recommendations and framing are types of nudging and whether they satisfy the requirement of adequate disclosure essential for a valid informed consent. He defines nudging as the systematic attempt at altering behaviour by non-rational means, and describes adequate disclosure as providing the patient with true information that enables (...) an understanding of treatment options and their risks and benefits. Simkulet argues that recommendations and framing are types of rational persuasion in that they potentially enhance patients’ understanding of treatment options and their risks and benefits. Therefore, not only do they not qualify as nudging, but they may be required in order to secure adequate disclosure. In this brief comment, I focus on Simkulet’s considerations of framing and show that framing may be nudging and that it may violate the requirements of informed consent. The framing of medical information may influence patients’ preferences for treatment. It has been shown that framing information about a risky medical intervention in chance of survival rather than risk of death increases patients’ preferences for the intervention.2 3 In his exploration of framing, Simkulet contends that the fact that people respond differently to …. (shrink)

This open access contributed volume examines the ethical and legal foundations of (future) policies on recommender systems and offers a transdisciplinary approach to tackle important issues related to their development, use and integration into online eco-systems. This volume scrutinizes the values driving automated recommendations - what is important for an individual receiving the recommendation, the company on which that platform was received, and society at large might diverge. The volume addresses concerns about manipulation of individuals and risks for personal (...) autonomy. From a legal perspective, the volume offers a much-needed evaluation of regulatory needs and lawmakers’ answers in various legal disciplines. The focus is on European Union measures of platform regulation, consumer protection and anti-discrimination law. The volume will be of particular interest to the community of legal scholars dealing with platform regulation and algorithmic decision making. By including specific use cases, the volume also exposes pitfalls associated with current models of regulation. Beyond the juxtaposition of purely ethical and legal perspectives, the volume contains truly interdisciplinary work on various aspects of recommender systems. (shrink)

Lao Tzu and Marx together provide the basis of an epistemology based on ignorance—not on knowledge. The failure of an epistemology based on knowledge is shown by the internal inconsistencies afflicting mainstream economics: Economic theory predicts certain empirical phenomena but these fail to reveal themselves. In fact, the strongest research finding in development economics is that there are almost no robust empirical relationships between an implemented policy and its desired outcomes in the real world. A theory of ignorance can highlight (...) many economic contradictions—and may even prove useful in understanding autistics. (shrink)

In his recent article ‘Nudging, Informed Consent and Bullshit’, William Simkulet 1 convincingly argues that certain types of nudging satisfy Frankfurt’s criteria of bullshit. As a prelude to this argument, Simkulet considers whether recommendations and framing are types of nudging and whether they satisfy the requirement of adequate disclosure essential for a valid informed consent. He defines nudging as the systematic attempt at altering behaviour by non-rational means, and describes adequate disclosure as providing the patient with true information that (...) enables an understanding of treatment options and their risks and benefits. Simkulet argues that recommendations and framing are types of rational persuasion in that they potentially enhance patients’ understanding of treatment options and their risks and benefits. Therefore, not only do they not qualify as nudging, but they may be required in order to secure adequate disclosure. In this brief comment, I focus on Simkulet’s considerations... (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)

People are increasingly subject to algorithmic decisions, and it is generally agreed that end-users should be provided an explanation or rationale for these decisions. There are different purposes that explanations can have, such as increasing user trust in the system or allowing users to contest the decision. One specific purpose that is gaining more traction is algorithmic recourse. We first pro- pose that recourse should be viewed as a recommendation problem, not an explanation problem. Then, we argue that the capability (...) approach provides plausible and fruitful ethical standards for re- course. We illustrate by considering the case of diversity constraints on algorithmic recourse. Finally, we discuss the significance and implications of adopting the capability approach for algorithmic recourse research. (shrink)

BackgroundThe International Committee of Medical Journal Editors Recommendations set ethical and editorial standards for article publication in most leading medical journals. Here, I examine the strengths and weaknesses of the Recommendations in the prevention of commercial bias in industry-financed journal literature, on three levels – scholarly discourse, article content, and article attribution.DiscussionWith respect to overall discourse, the most important measures in the ICMJE Recommendations are for enforcing clinical trial registration and controlling duplicate publication. With respect to article (...) content, the ICMJE promotes stringent author accountability and adherence to established reporting standards. However, the ICMJE accepts the use of commercial editorial teams to produce manuscripts, which is a potential source of bias, and accepts private company ownership and analysis of clinical trial data. New ICMJE guidance on data sharing will address but not eliminate problems of commercial data access. With respect to attribution, the Recommendations oppose guest authorship and encourage clear documentation of author contributions. However, they exclude writers from coauthorship; provide no specific advice on the attribution of commercial literature, for instance with respect to company authorship, author sequence or prominent commercial labeling; and endorse the use of fine print and euphemism. The ICMJE requires detailed author interest disclosures, but overlooks the interests of non-authors and companies, and does not recommend that interests most salient to the publication are highlighted. Together, these weaknesses facilitate “advocacy”-based marketing, in which literature planned, financed and produced by companies is fronted by academics, enabling commercial messages to be presented to customers by their respected clinical peers rather than companies themselves.ConclusionsThe ICMJE Recommendations set important research and reporting standards, without which commercial bias would likely be a significantly greater problem than it is today. However, they also support practices of commercial data control, content development and attribution that run counter to science’s values of openness, objectivity and truthfulness. These weaknesses could be addressed with appropriate modifications to the Recommendations. The ICMJE should also disclose its own commercial interests and funding – not least because publishing organizations that finance it and pay the salaries of some member editors derive substantial revenues from industry. (shrink)

We study whether changes in analyst recommendation ratings systems encouraged by the implementation of NASD 2711 in 2002 are associated with improved objectivity and independence in analyst recommendations. Using recommendations issued during windows surrounding major investment banking events, we show that reductions in analyst optimism following the reforms concentrate in the recommendations of analysts whose employer adopted a three-tier rating system at the time of the reforms, and that this effect is generally stronger for analysts whom the (...) underlying incentives to engage in unethical behaviour is greatest. We also find evidence that adoption of the three-tier system improved the market’s perception of the objectivity of analyst recommendations issued after SEOs, and that for hold and sell-type recommendations this effect was stronger for analysts subject to the greatest potential ethical conflicts. While we find some evidence of a general post-reform increase in the profitability of recommendations issued following equity transactions, this improvement was only conditioned by changes to the rating system in our IPO sample. (shrink)

YouTube has been implicated in the transformation of users into extremists and conspiracy theorists. The alleged mechanism for this radicalizing process is YouTube’s recommender system, which is optimized to amplify and promote clips that users are likely to watch through to the end. YouTube optimizes for watch-through for economic reasons: people who watch a video through to the end are likely to then watch the next recommended video as well, which means that more advertisements can be served to them. This (...) is a seemingly innocuous design choice, but it has a troubling side-effect. Critics of YouTube have alleged that the recommender system tends to recommend extremist content and conspiracy theories, as such videos are especially likely to capture and keep users’ attention. To date, the problem of radicalization via the YouTube recommender system has been a matter of speculation. The current study represents the first systematic, pre-registered attempt to establish whether and to what extent the recommender system tends to promote such content. We begin by contextualizing our study in the framework of technological seduction. Next, we explain our methodology. After that, we present our results, which are consistent with the radicalization hypothesis. Finally, we discuss our findings, as well as directions for future research and recommendations for users, industry, and policy-makers. (shrink)

BackgroundThe International Committee of Medical Journal Editors Recommendations set ethical and editorial standards for article publication in most leading medical journals. Here, I examine the strengths and weaknesses of the Recommendations in the prevention of commercial bias in industry-financed journal literature, on three levels – scholarly discourse, article content, and article attribution.DiscussionWith respect to overall discourse, the most important measures in the ICMJE Recommendations are for enforcing clinical trial registration and controlling duplicate publication. With respect to article (...) content, the ICMJE promotes stringent author accountability and adherence to established reporting standards. However, the ICMJE accepts the use of commercial editorial teams to produce manuscripts, which is a potential source of bias, and accepts private company ownership and analysis of clinical trial data. New ICMJE guidance on data sharing will address but not eliminate problems of commercial data access. With respect to attribution, the Recommendations oppose guest authorship and encourage clear documentation of author contributions. However, they exclude writers from coauthorship; provide no specific advice on the attribution of commercial literature, for instance with respect to company authorship, author sequence or prominent commercial labeling; and endorse the use of fine print and euphemism. The ICMJE requires detailed author interest disclosures, but overlooks the interests of non-authors and companies, and does not recommend that interests most salient to the publication are highlighted. Together, these weaknesses facilitate “advocacy”-based marketing, in which literature planned, financed and produced by companies is fronted by academics, enabling commercial messages to be presented to customers by their respected clinical peers rather than companies themselves.ConclusionsThe ICMJE Recommendations set important research and reporting standards, without which commercial bias would likely be a significantly greater problem than it is today. However, they also support practices of commercial data control, content development and attribution that run counter to science’s values of openness, objectivity and truthfulness. These weaknesses could be addressed with appropriate modifications to the Recommendations. The ICMJE should also disclose its own commercial interests and funding – not least because publishing organizations that finance it and pay the salaries of some member editors derive substantial revenues from industry. (shrink)

Maintaining academic integrity is a growing concern for higher education, increasingly so due to the pivot to remote learning in 2020 caused by the COVID-19 pandemic. We canvassed students, faculty, and tutors at an online Canadian university about their perspectives on academic integrity and misconduct. The survey asked how the university could improve policies concerning issues of academic integrity, how faculty and tutors handled cases of misconduct, about satisfaction with how academic violations were treated, and about the role of students, (...) faculty, and tutors in encouraging academic integrity. As well, we collected suggestions from respondents for reducing cheating, addressing academic misconduct, and general ideas about academic integrity. The distinction between misconduct and integrity was not always clear in their comments. We received responses from 228 students and 73 faculty and tutors, generating hundreds of comments. In this paper we focus only on the answers to open-ended questions. Using content analysis, we categorized the replies into similar threads. After multiple iterations of analysis, we extracted three general recommendation groupings: Policy and Procedures, Compliance and Commitment, and Resources. Based on respondents’ views, we propose a balanced approach to supporting academic integrity. Although we conducted the study pre-COVID-19, the recommendations apply to current and future academic integrity practices in our context and beyond. (shrink)

Infonorma is a multi-agent system that provides its users with recommendations of legal normative instruments they might be interested in. The Filter agent of Infonorma classifies normative instruments represented as Semantic Web documents into legal branches and performs content-based similarity analysis. This agent, as well as the entire Infonorma system, was modeled under the guidelines of MAAEM, a software development methodology for multi-agent application engineering. This article describes the Infonorma requirements specification, the architectural design solution for those requirements, the (...) detailed design of the Filter agent and the implementation model of Infonorma, according to the guidelines of the MAAEM methodology. (shrink)