Search

Robotics and artificial intelligence (AI) are revolutionizing all spheres of human life. From industrial processes to graphic design, the implementation of automated intelligent systems is changing how industries work. The spread of robots and AI systems has triggered academic institutions to closely examine how these technologies may affect the humanity—this is how the fields of roboethics and AI ethics have been born. The identification of ethical issues for robotics and AI and creation of ethical frameworks were the first steps to creating a regulatory environment for these technologies. In this paper, we focus on regulatory efforts in Europe and North America to create enforceable regulation for AI and robotics. We describe and compare ethical principles, policies, and regulations that have been proposed by government organizations for the design and use of robots and AI. We also discuss proposed international regulation for robotics and AI. This paper tries to highlight the need for a comprehensive, enforceable, and agile policy to ethically regulate technology today and in the future. Through reviewing existing policies, we conclude that the European Unition currently leads the way in defining roboethics and AI ethical principles and implementing them into policy. Our findings suggest that governments in Europe and North America are aware of the ethical risks that robotics and AI pose, and are engaged in policymaking to create regulatory policies for these new technologies.

Background Digital health research repositories propose sharing longitudinal streams of health records and personal sensing data between multiple projects and researchers. Motivated by the prospect of personalizing patient care (precision medicine), these initiatives demand broad public acceptance and large numbers of data contributors, both of which are challenging. Objective This study investigates public attitudes toward possibly contributing to digital health research repositories to identify factors for their acceptance and to inform future developments. Methods A cross-sectional online survey was conducted from March 2020 to December 2020. Because of the funded project scope and a multicenter collaboration, study recruitment targeted young adults in Denmark and Brazil, allowing an analysis of the differences between 2 very contrasting national contexts. Through closed-ended questions, the survey examined participants’ willingness to share different data types, data access preferences, reasons for concern, and motivations to contribute. The survey also collected information about participants’ demographics, level of interest in health topics, previous participation in health research, awareness of examples of existing research data repositories, and current attitudes about digital health research repositories. Data analysis consisted of descriptive frequency measures and statistical inferences (bivariate associations and logistic regressions). Results The sample comprises 1017 respondents living in Brazil (1017/1600, 63.56%) and 583 in Denmark (583/1600, 36.44%). The demographics do not differ substantially between participants of these countries. The majority is aged between 18 and 27 years (933/1600, 58.31%), is highly educated (992/1600, 62.00%), uses smartphones (1562/1600, 97.63%), and is in good health (1407/1600, 87.94%). The analysis shows a vast majority were very motivated by helping future patients (1366/1600, 85.38%) and researchers (1253/1600, 78.31%), yet very concerned about unethical projects (1219/1600, 76.19%), profit making without consent (1096/1600, 68.50%), and cyberattacks (1055/1600, 65.94%). Participants’ willingness to share data is lower when sharing personal sensing data, such as the content of calls and texts (1206/1600, 75.38%), in contrast to more traditional health research information. Only 13.44% (215/1600) find it desirable to grant data access to private companies, and most would like to stay informed about which projects use their data (1334/1600, 83.38%) and control future data access (1181/1600, 73.81%). Findings indicate that favorable attitudes toward digital health research repositories are related to a personal interest in health topics (odds ratio [OR] 1.49, 95% CI 1.10-2.02; P=.01), previous participation in health research studies (OR 1.70, 95% CI 1.24-2.35; P=.001), and awareness of examples of research repositories (OR 2.78, 95% CI 1.83-4.38; P Conclusions This study reveals essential factors for acceptance and willingness to share personal data with digital health research repositories. Implications include the importance of being more transparent about the goals and beneficiaries of research projects using and re-using data from repositories, providing participants with greater autonomy for choosing who gets access to which parts of their data, and raising public awareness of the benefits of data sharing for research. In addition, future developments should engage with and reduce risks for those unwilling to participate.

This paper examines ethics in international trade from several different ethical perspectives, including consequentialism, utilitarianism, virtue ethics and deontology. The views of Aristotle, Plato, Adam Smith, Bentham, Kant, Pareto, Rawls, Flew, Nozick, Bastiat and others are discussed and contrasted. Flow charts are provided to reflect the utilitarian and rights approaches to trade. Utilitarianism is shown to be structurally deficient for several reasons that cannot be overcome. Antidumping laws and sanctions are shown to be negative-sum games. Antidumping laws are seen for what they are, tools of rent-seeking in which some influential minority seeks to use the powers of government to protect them from competition, which harms the vast majority of the population. Any trade restrictions must necessarily violate someone’s property, contract and/or association rights. The only way to achieve a just trade policy is to allow individuals, and the corporations that represent them, to trade what they have for what they want without government interference. The view that trade deficits are bad and trade surpluses are good is dismantled. The present paper also includes a bibliography on trade, including links to more than 100 studies on trade.

Within the Computer Science community, many ethical issues have emerged as significant and critical concerns. Computer ethics is an academic field in its own right and there are unique ethical issues associated with information technology. It encompasses a range of issues and concerns including privacy and agency around personal information, Artificial Intelligence and pervasive technology, the Internet of Things and surveillance applications. As computing technology impacts society at an ever growing pace, there are growing calls for more computer ethics content to be included in Computer Science curricula. In this paper we present the results of a survey that polled faculty from Computer Science and related disciplines about teaching practices for computer ethics at their institutions. The survey was completed by respondents from 61 universities across 23 European countries. Participants were surveyed on whether or not computer ethics is taught to Computer Science students at each institution, the reasons why computer ethics is or is not taught, how computer ethics is taught, the background of staff who teach computer ethics and the scope of computer ethics curricula. This paper presents and discusses the results of the survey.

Digital tools play an important role in fighting the current global COVID-19 pandemic. We conducted a representative online study in Germany on a sample of 599 participants to evaluate the user perception of vaccination certificates. We investigated five different variants of vaccination certificates, based on deployed and planned designs in a between-group design, including paper-based and app-based variants. Our main results show that the willingness to use and adopt vaccination certificates is generally high. Overall, paper-based vaccination certificates were favored over app-based solutions. The willingness to use digital apps decreased significantly by a higher disposition to privacy, and increased by higher worries about the pandemic and acceptance of the coronavirus vaccination. Vaccination certificates resemble an interesting use case for studying privacy perceptions for health related data. We hope that our work will be able to educate the currently ongoing design of vaccination certificates, will give us deeper insights into privacy of health-related data and apps, and prepare us for future potential applications of vaccination certificates and health apps in general., Comment: 22 pages, 4 figures, 5 tables

Adolescents with chronic health conditions transitioning from pediatric to adult healthcare services experience a wide array of difficulties. In response, transition programs have been developed. Transition programs commonly embody goals such as autonomy and independence. However, these may not be highly valued by young adults and their families. To assess critically the current evaluation of goals and outcomes of these programs, the concept of human flourishing offers a promising alternative to concepts of quality of life. It grants that objectives pursued (e.g., health, social integration) stand to be interpreted by the agent as being valuable and coherent with their meaning-making narrative. Flourishing is also an indicator of physical and psychological health. The purpose of this paper is to review the literature on transition care to assess whether and how flourishing is addressed in the evaluation of transition programs. We carried out systematic sampling of the literature and applied a qualitative thematic content extraction strategy. Based on Ryff and Singer's integrative concept of flourishing, we examined whether six key dimensions of flourishing (self-acceptance, positive relations with others, personal growth, purpose in life, environmental mastery, and autonomy) were present in current evaluation practices. We reviewed 105 relevant papers and found that (1) 44 out of 105 articles evaluated one or more dimensions of human flourishing; (2) there was considerable variation in the assessment of these dimensions, which was sometimes minimalistic; (3) no single evaluation was based on an explicit measure of human flourishing; (4) autonomy and positive relationships were the dimensions most investigated; (5) the evaluation of transition care mostly emphasized medical aspects of health. Considering its lifelong impact, it is crucial to better understand how transition care can support the flourishing of young adults. Open-ended views on flourishing based on participatory and collaborative research designs should be explored in this context.

Business intelligence and AI services often involve the collection of copious amounts of multidimensional personal data. Since these data usually contain sensitive information of individuals, the direct collection can lead to privacy violations. Local differential privacy (LDP) is currently considered a state-ofthe-art solution for privacy-preserving data collection. However, existing LDP algorithms are not applicable to high-dimensional data; not only because of the increase in computation and communication cost, but also poor data utility.In this paper, we aim at addressing thecurse-of-dimensionalityproblem in LDP-based high-dimensional data collection. Based on the idea of machine learning and data synthesis, we propose DP-Fed-Wae, an efficient privacy-preserving framework for collecting high-dimensional categorical data. With the combination of a generative autoencoder, federated learning, and differential privacy, our framework is capable of privately learning the statistical distributions of local data and generating high utility synthetic data on the server side without revealing users’ private information. We have evaluated the framework in terms of data utility and privacy protection on a number of real-world datasets containing 68–124 classification attributes. We show that our framework outperforms the LDP-based baseline algorithms in capturing joint distributions and correlations of attributes and generating high-utility synthetic data. With a local privacy guarantee ∈ = 8, the machine learning models trained with the synthetic data generated by the baseline algorithm cause an accuracy loss of 10% ~ 30%, whereas the accuracy loss is significantly reduced to less than 3% and at best even less than 1% with our framework. Extensive experimental results demonstrate the capability and efficiency of our framework in synthesizing high-dimensional data while striking a satisfactory utility-privacy balance.

Fair exchange protocols are among the most important cryptographic primitives in electronic commerce. A basic fair exchange protocol requires that two parties who want to exchange their digital items either receive what they have been promised, or lose nothing. Privacy of fair exchange requires that no one else (other than the two parties) learns anything about the items. Fairness and privacy have been considered as two distinct properties of an exchange protocol. In this paper, we show that subtle ways of leaking the exchange item to the third parties affect fairness in fair exchange protocols when the item is confidential. Our focus is on Fair-Swap, a recently proposed fair exchange protocol that uses a smart contract for dispute resolution, has proven security in UC (Universal Composability) framework, and provides privacy when both parties are honest. We demonstrate, however, that FairSwap’s dispute resolution protocol leaks information to the public and this leakage provides opportunities for the dishonest parties to influence the protocol’s fairness guarantee. We then propose an efficient privacy-enhanced version of Fair-Swap, prove its security and give an implementation and performance evaluation of our proposed system. Our privacy enhancement uses circuit randomization, and we prove its security and privacy in an extension of universal composability model for non-monolithic adversaries that would be of independent interest.

A membership inference attack (MIA) against a machine-learning model enables an attacker to determine whether a given data record was part of the model's training data or not. In this paper, we provide an in-depth study of the phenomenon of disparate vulnerability against MIAs: unequal success rate of MIAs against different population subgroups. We first establish necessary and sufficient conditions for MIAs to be prevented, both on average and for population subgroups, using a notion of distributional generalization. Second, we derive connections of disparate vulnerability to algorithmic fairness and to differential privacy. We show that fairness can only prevent disparate vulnerability against limited classes of adversaries. Differential privacy bounds disparate vulnerability but can significantly reduce the accuracy of the model. We show that estimating disparate vulnerability to MIAs by na\"ively applying existing attacks can lead to overestimation. We then establish which attacks are suitable for estimating disparate vulnerability, and provide a statistical framework for doing so reliably. We conduct experiments on synthetic and real-world data finding statistically significant evidence of disparate vulnerability in realistic settings. The code is available at https://github.com/spring-epfl/disparate-vulnerability, Comment: To appear in Privacy-Enhancing Technologies Symposium (PETS) 2022. This version has an updated authors list

In this paper, we explore and describe what is needed to allow connected and automated vehicles (CAVs) to break traffic rules in order to minimise road safety risk and to operate with appropriate transparency (according to recommendation 4 in Bonnefon et al., European Commission, 2020). Reviewing current traffic rules with particular reference to two driving situations (speeding and mounting the pavement), we illustrate why current traffic rules are not suitable for CAVs and why making new traffic rules specifically for CAVs would be inappropriate. In defining an alternative approach to achieving safe CAV driving behaviours, we describe the use of ethical goal functions as part of hybrid AI systems, suggesting that functions should be defined by governmental bodies with input from citizens and stakeholders. Ethical goal functions for CAVs would enable developers to optimise driving behaviours for safety under conditions of uncertainty whilst allowing for differentiation of products according to brand values. Such functions can differ between regions according to preferences for safety behaviours within that region and can be updated over time, responding to continual socio-technological feedback loops. We conclude that defining ethical goal functions is an urgent and necessary step from governmental bodies to enable the safe and transparent operation of CAVs and accelerate the reduction in road casualties they promise to achieve.

Critics of artificial intelligence have suggested that the principles of fairness, accountability and transparency (FATE) have been used for ‘ethics washing’, in order to appease industrial interests. In this article, we develop this relational and context-dependent analysis, arguing that ethics should not be understood as abstract values or design decisions, but as socio-technical achievements, enacted in the practices of students, teachers and corporations. We propose that the ethics of using AI in education are political, involving the distribution of power, privilege and resources. To illustrate this, we trace the controversies that followed from an incident in which a student was misclassified as a cheat by an online proctoring platform during the Covid-19 lockdown, analysing this incident to reveal the socio-technical arrangements of academic integrity. We then show how Joan Tronto’s work on the ethics of care can help think about the politics of these socio-technical arrangements — that is, about historically constituted power relations and the delegation of responsibilities within these institutions. The paper concludes by setting the immediate need for restorative justice against the slower temporality of systemic failure, and inviting speculation that could create new relationships between universities, students, businesses, algorithms and the idea of academic integrity.

Publisher Copyright: © 2021, The Author(s). Foreign direct investment (FDI) in offshore financial centers (OFCs) is gaining increased attention in business ethics research. Much of this research tends to focus on OFCs as locations where firms can avoid taxes, considering such behavior as unethical. Yet, there is dearth of studies on capital round-tripping by emerging market firms, which is an integral part of this phenomenon. Such round-tripping involves firms sending capital into OFCs only to invest it back in the home country under the guise of “foreign” investment. Presently there is little discussion of the ethical implications of such round-trip FDI activities. In this paper, we conceptualize round-tripping as institutional arbitrage and look at the determinants and ethical implications of such investments into OFCs. Exploring Russian round-tripping we note that firms tend to invest more funds in OFCs that offer a combination of tax and secrecy, or secrecy and property rights protection arbitrage opportunities. In either case firms exploit the opportunities provided by institutional differences between the OFC and Russia while investing back into Russia. Our results tend to indicate that equating OFC investment to tax avoidance and thus deeming it as unethical behavior is too narrow an explanation in the case of emerging economy round-tripping. This is because such investments are often motivated by the unethical behavior of home country stakeholders and may in fact provide benefits to society.

Mainstream sports media generate a football information overload that sometimes makes it difficult to separate rumours from real news. Accordingly, this paper analyses the level of misinfor- mation in the coverage of the 2020 winter football transfer window in four leading European digital sports media outlets: Marca (Spain), A Bola (Portugal), La Gazzetta (Italy) and The Guardian Sport (Britain). The methodology used was based on the content analysis of hundreds of news pieces and tweets posted on these outlets’ football homepages and Twitter handles over a month. To examine to what extent this coverage may have been speculative, misleading or false, the misinformation matrix developed by the fact-checking organisation First Draft News was employed to classify five different types of inaccuracies in sports reporting. A system was also created to determine how many reported rumours finally turned out to be true, which sources were more reliable and what outlets resulted more accurate. The findings reveal that the four digital media published a larger amount of non-factual content about likely football deals rather than sealed transfers. Speculative reporting prevailed in the coverage of the top teams in each league, on which the media outlets placed the accent, whereas reporting about minor clubs was based more on factual news.

In April 2020, close to the start of the first U.K. COVID-19 lockdown, the U.K. government announced the development of a COVID-19 contact tracing app, which was later trialled on the U.K. island, the Isle of Wight, in May/June 2020. United Kingdom surveys found general support for the development of such an app, which seemed strongly influenced by public trust. Institutions developing the app were called upon to fulfil the commitment to public trust by acting with trustworthiness. Such calls presuppose that public trust associated with the app can emerge if the conditions for trustworthiness are met and that public trust is simplistic, i.e., linearly the sum of each member of the publics’ individual – U.K. government trust relationship. Drawing on a synthesis of the trust literature and fifteen interviews with members of the public trialling the app on the Isle of Wight, this paper aims to explore what trust mechanisms and relationships are at play when thinking about public trust in the context of the U.K. COVID-19 app. We argue that public trust is a complex social phenomenon and not linearly correlated with institutional trustworthiness. As such, attention needs to widen from calls for trustworthy infrastructures as a way to build public trust, to a deeper understanding of those doing the trusting; in particular, what or whom do people place their trust in (or not) when considering whether using the app and why. An understanding of this will help when trying to secure public trust during the implementation of necessary public health measures. Supplementary Information The online version contains supplementary material available at 10.1007/s11673-021-10127-x.

The Covid-19 pandemic has impacted multiple facets of our lives and created a number of legal and ethical dilemmas. One of the greatest challenges at present is the production and distribution of the Covid-19 vaccine. Refusing to supply Covid vaccines widely could affect millions worldwide, and the pandemic may last for a long time. The competition authorities’ monitoring of the health sector in many countries has been subject to changes in thecurrent crisis. The question is whether we can force the Covid-19 vaccine manufacturers, legally and ethically, to sell their products and share their information with their competitors. Furthermore, what are the post-pandemic consequences of policies adopted during the pandemic? This paper employs a descriptive-analytical method to examine the importance of competition and intellectual property policies as they relate to Covid-19. It concludes that instead of focusing on individual rights in a crisis, public rights need to be emphasised. However, we should not underestimate the post-pandemic consequences of policies adopted during the Covid-19 pandemic.

Despite the prevalence of Internet of Things (IoT) devices, there is little information about the purpose and risks of the Internet traffic these devices generate, and consumers have limited options for controlling those risks. A key open question is whether one can mitigate these risks by automatically blocking some of the Internet connections from IoT devices, without rendering the devices inoperable. In this paper, we address this question by developing a rigorous methodology that relies on automated IoT-device experimentation to reveal which network connections (and the information they expose) are essential, and which are not. We further develop strategies to automatically classify network traffic destinations as either required (i.e., their traffic is essential for devices to work properly) or not, hence allowing firewall rules to block traffic sent to non-required destinations without breaking the functionality of the device. We find that indeed 16 among the 31 devices we tested have at least one blockable non-required destination, with the maximum number of blockable destinations for a device being 11. We further analyze the destination of network traffic and find that all third parties observed in our experiments are blockable, while first and support parties are neither uniformly required or non-required. Finally, we demonstrate the limitations of existing blocklists on IoT traffic, propose a set of guidelines for automatically limiting non-essential IoT traffic, and we develop a prototype system that implements these guidelines.

Technological progress can disrupt domains and change the way we work and collaborate. This paper presents a qualitative study with 52 German farmers that investigates the impact of the ongoing digitalization process in agriculture and discusses the implications for privacy research. As in other domains, the introduction of digital tools and services leads to the data itself becoming a resource. Sharing this data with products along the supply chain is favored by retailers and consumers, who benefit from traceability through transparency. However, transparency can pose a privacy risk. Having insight into the business data of others along the supply chain provides an advantage in terms of market position. This is particularly true in agriculture, where there is already a significant imbalance of power between actors. A multitude of small and medium-sized farming businesses are opposed by large upstream and downstream players that drive technological innovation. Further weakening the market position of farmers could lead to severe consequences for the entire sector. We found that on the one hand, privacy behaviors are affected by adoption of digitalization, and on the other hand, privacy itself influences adoption of digital tools. Our study sheds light on the emerging challenges for farmers and the role of privacy in the process of digitalization in agriculture.

Rapid developments in evolutionary computation, robotics, 3D-printing, and material science are enabling advanced systems of robots that can autonomously reproduce and evolve. The emerging technology of robot evolution challenges existing AI ethics because the inherent adaptivity, stochasticity, and complexity of evolutionary systems severely weaken human control and induce new types of hazards. In this paper we address the question how robot evolution can be responsibly controlled to avoid safety risks. We discuss risks related to robot multiplication, maladaptation, and domination and suggest solutions for meaningful human control. Such concerns may seem far-fetched now, however, we posit that awareness must be created before the technology becomes mature.

open access article The concept of innovation ecosystems has become prominent due to its explanatory power. It offers a convincing account of innovation, explaining how and why innovation pathways change and evolve. It has been adopted to explain, predict, and steer innovation. The increasing importance of innovation for most aspects of human life calls for the inclusion of ethical and social rights aspects into the innovation ecosystems discourse. The current innovation ecosystems literature does not provide guidance on how the integration of ethical and social concerns into innovation ecosystems can be realised. One way to achieve this is to draw on the discussion of responsible research and innovation (RRI). This paper applies RRI to the innovation ecosystems discourse and proposes the concept of responsible innovation systems. It draws on the discussion of the ethics of artificial intelligence (AI) to explore how responsible AI innovation ecosystems can be shaped and realised.

Background An increasing number of HIV cure trials involve combining multiple potentially curative interventions. Until now, considerations for designing and implementing complex combination HIV cure trials have not been thoroughly considered. Methods We used a purposive method to select key informants for our study. Informants included biomedical HIV cure researchers, regulators, policy makers, bioethicists, and community members. We used in-depth interviews to generate ethical and practical considerations to guide the design and implementation of combination HIV cure research. We analyzed the qualitative data using conventional content analysis focused on inductive reasoning. Results We interviewed 11 biomedical researchers, 4 community members, 2 regulators, 1 policy researcher, and 1 bioethicist. Informants generated considerations for designing and implementing combination interventions towards an HIV cure, focused on ethical aspects, as well as considerations to guide trial design, benefit/risk determinations, regulatory requirements, prioritization and sequencing and timing of interventions, among others. Informants also provided considerations related to combining specific HIV cure research modalities, such as broadly neutralizing antibodies (bNAbs), cell and gene modification products, latency-reversing agents and immune-based interventions. Finally, informants provided suggestions to ensure meaningful therapeutic improvements over standard antiretroviral therapy, overcome challenges of designing combination approaches, and engage communities around combination HIV cure research. Conclusion The increasing number of combination HIV cure trials brings with them a host of ethical and practical challenges. We hope our paper will inform meaningful stakeholder dialogue around the use of combinatorial HIV cure research approaches. To protect the public trust in HIV cure research, considerations should be periodically revisited and updated with key stakeholder input as the science continues to advance.

Qualitative fieldwork research on sensitive topics sometimes requires that interviewees be granted confidentiality and anonymity. When qualitative researchers later publish their findings, they must ensure that any statements obtained during fieldwork interviews cannot be traced back to the interviewees. Given these protections to interviewees, the integrity of the published findings cannot usually be verified or replicated by third parties, and the scholarly community must trust the word of qualitative researchers when they publish their results. This trust is fundamentally abused, however, when researchers publish articles reporting qualitative fieldwork data that they never collected. Using only publicly available information, I argue that a 2017 article in an Elsevier foreign policy and international relations journal presents anonymised fieldwork interviews that could not have occurred as described. As an exercise in post-publication peer review (PPPR), this paper examines the evidence that calls into question the reliability of the putative fieldwork quotations. I show further that the 2017 article is not a unique case. The anonymity and confidentiality protections common in some areas of research create an ethical problem: the protections necessary for obtaining research data can be used as a cover to hide substandard research practices as well as research misconduct.