Current advances in research, development and application of artificial intelligence systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, (...) I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems—and how the effectiveness in the demands of AI ethics can be improved. (shrink)

The prospect of consumable meat produced in a laboratory setting without the need to raise and slaughter animals is both realistic and exciting. Not only could such in vitro meat become popular due to potential cost savings, but it also avoids many of the ethical and environmental problems with traditional meat productions. However, as with any new technology, in vitro meat is likely to face some detractors. We examine in detail three potential objections: 1) in vitro meat is disrespectful, either (...) to nature or to animals; 2) it will reduce the number of happy animals in the world; and 3) it will open the door to cannibalism. While each objection has some attraction, we ultimately find that all can be overcome. The upshot is that in vitro meat production is generally permissible and, especially for ethical vegetarians, worth promoting. (shrink)

In the original publication of this article, the Table 1 has been published in a low resolution. Now a larger version of Table 1 is published in this correction. The publisher apologizes for the error made during production.

Many ethics initiatives have stipulated sets of principles and standards for good technology development in the AI sector. However, several AI ethics researchers have pointed out a lack of practical realization of these principles. Following that, AI ethics underwent a practical turn, but without deviating from the principled approach. This paper proposes a complementary to the principled approach that is based on virtue ethics. It defines four “basic AI virtues”, namely justice, honesty, responsibility and care, all of which represent specific (...) motivational settings that constitute the very precondition for ethical decision making in the AI field. Moreover, it defines two “second-order AI virtues”, prudence and fortitude, that bolster achieving the basic virtues by helping with overcoming bounded ethicality or hidden psychological forces that can impair ethical decision making and that are hitherto disregarded in AI ethics. Lastly, the paper describes measures for successfully cultivating the mentioned virtues in organizations dealing with AI research and development. (shrink)

This article explores Franz Brentano's opposition to papal infallibility, and sets this in the context of his development as a Catholic scholar. An active participant in the Catholic revival of the nineteenth century, Brentano contributed to the emergence of neoscholasticism through his philosophical rehabilitation of Aristotle. Brentano ultimately left the Church however as the result of intensive scrutiny of his faith occasioned by his analysis of infallibility. This article explores this crisis of faith by analyzing, for the first time, Brentano's (...) position on papal infallibility, and suggesting ways that it informed his philosophical development. (shrink)

We introduce the Bounded Axiom A Forcing Axiom . It turns out that it is equiconsistent with the existence of a regular ∑2-correct cardinal and hence also equiconsistent with BPFA. Furthermore we show that, if consistent, it does not imply the Bounded Proper Forcing Axiom.

In highly structured organisational fields individual efforts to deal rationally with uncertainty and constraints tend to lead, in the aggregate, to greater homogeneity in structure, culture and output. Drawing on institutional theory, this paper develops research propositions regarding the nature and scope of corporate social responsibility (CSR) engagement at trade/industry association level. The cases of the water and sewerage and film industries are used in order to test these propositions. The findings suggest that (a) trade associations in more homogeneous industries (...) are more likely to engage with CSR‐related issues; (b) trade associations in industries that face greater external scrutiny and threats to legitimacy are more likely to engage with CSR‐related issues; and (c) trade associations are more likely to engage with those substantive CSR issues that are of greater concern to the industry's most salient stakeholders. The findings also suggest that trade associations may have a greater tendency to engage in symbolic legitimation efforts through CSR if faced with the task of repairing industry legitimacy. (shrink)

We live in the Information Age. Advances over the past 50 years in computing technology have enabled ever-expanding capacity to generate, store, transfer, process and analyse information about people, societies, products, services, the environment—nearly every aspect of the world. In parallel, concerns over how such data is being used have emerged, focusing especially on issues of privacy and confidentiality. Yet as technological capabilities continue to expand, the debate over ethical uses of data inevitably has evolved as well. Whereas we used (...) to just talk about data, now there is great concern over Big Data. This chapter explores what, exactly, Big Data is; the ethical ramifications of its emergence, particularly for consent and anonymization; and what sorts of policy solutions are being proposed to address these emerging challenges. We will not defend one particular approach, but hope this chapter can be a useful reference for those who are contemplating difficult questions surrounding responsible management of Big Data. (shrink)

The current “AI Summer” is marked by scientific breakthroughs and economic successes in the fields of research, development, and application of systems with artificial intelligence. But, aside from the great hopes and promises associated with artificial intelligence, there are a number of challenges, shortcomings and even limitations of the technology. For one, these challenges arise from methodological and epistemological misconceptions about the capabilities of artificial intelligence. Secondly, they result from restrictions of the social context in which the development of applications (...) of machine learning is embedded. And third, they are a consequence of current technical limitations in the development and use of artificial intelligence. The paper intends to provide an overview of current challenges which the research and development of applications in the field of artificial intelligence and machine learning have to face, whereas all three mentioned areas are to be further explored in this paper. (shrink)

The term complementarity plays a central role in quantum physics, not least in various approaches to defining entanglement and the conditions for its occurrence. It has, however, been used in a variety of ways by different authors, denoting different concepts and relationships. Here we describe and clarify some of them and analyze the role they play with respect to the phenomenon of entanglement. Based on these considerations we discuss the recently proposed system-theoretical generalization of the concepts entanglement and complementarity (Atmanspacher (...) et al. in Found Phys 32(3):379–406, 2002; von Lucadou et al. in J Conscious Stud 14(4):50–74, 2007; Filk and Römer in Axiomathes 21(2):211–220, 2011; Walach and Von Stillfried in Axiomathes 21(2): 185–209, 2011). We hope that a clarification regarding the specific meaning of these terms can be useful to the growing engagement with this interesting hypothesis and its critical investigation. (shrink)

The term complementarity plays a central role in quantum physics, not least in various approaches to defining entanglement and the conditions for its occurrence. It has, however, been used in a variety of ways by different authors, denoting different concepts and relationships. Here we describe and clarify some of them and analyze the role they play with respect to the phenomenon of entanglement. Based on these considerations we discuss the recently proposed system-theoretical generalization of the concepts entanglement and complementarity (Atmanspacher (...) et al. in Found Phys 32(3):379–406, 2002; von Lucadou et al. in J Conscious Stud 14(4):50–74, 2007; Filk and Römer in Axiomathes 21(2):211–220, 2011; Walach and Von Stillfried in Axiomathes 21(2): 185–209, 2011). We hope that a clarification regarding the specific meaning of these terms can be useful to the growing engagement with this interesting hypothesis and its critical investigation. (shrink)

Certain research strands can yield “forbidden knowledge”. This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance, with regard to generative video or text synthesis, (...) personality analysis, behavior manipulation, software vulnerability detection and the like. Up till now, the machine learning research community embraces the idea of open access. However, this is opposed to precautionary efforts to prevent the malicious use of machine learning applications. Information about or from such applications may, if improperly disclosed, cause harm to people, organizations or whole societies. Hence, the goal of this work is to outline deliberations on how to deal with questions concerning the dissemination of such information. It proposes a tentative ethical framework for the machine learning community on how to deal with forbidden knowledge and dual-use applications. (shrink)

Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, Health and (...) Policy-relevant Ethics in Singapore Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data. (shrink)

Industry involvement in the machine learning (ML) community seems to be increasing. However, the quantitative scale and ethical implications of this influence are rather unknown. For this purpose, we have not only carried out an informed ethical analysis of the field, but have inspected all papers of the main ML conferences NeurIPS, CVPR, and ICML of the last 5 years—almost 11,000 papers in total. Our statistical approach focuses on conflicts of interest, innovation, and gender equality. We have obtained four main (...) findings. (1) Academic–corporate collaborations are growing in numbers. At the same time, we found that conflicts of interest are rarely disclosed. (2) Industry papers amply mention terms that relate to particular trending machine learning topics earlier than academia does. (3) Industry papers are not lagging behind academic papers with regard to how often they mention keywords that are proxies for social impact considerations. (4) Finally, we demonstrate that industry papers fall short of their academic counterparts with respect to the ratio of gender diversity. We believe that this work is a starting point for an informed debate within and outside of the ML community. (shrink)

The future of health research will be characterised by three continuing trends: rising demand for health data; increasing impracticability of obtaining specific consent for secondary research; and decreasing capacity to effectively anonymise data. In this context, governments, clinicians and the research community must demonstrate that they can be responsible stewards of health data. IRBs and RECs sit at heart of this process because in many jurisdictions they have the capacity to grant consent waivers when research is judged to be of (...) particular value. However, several different terms are used to refer to this value, indicating a lack of conceptual clarity regarding the appropriate test for access to health data for research without consent. In this paper we do three things. First we describe the current confusion and instability in terminology relating to public interest in the context of consent waivers. Second we argue for harmonisation of terminology on the grounds of clarity, transparency and consistency. Third we argue that the term ‘public interest’ best reflects the normative work required to justify consent waivers because it is the broadest of the competing terms. ‘Public interest’ contains within its scope positive and negative implications of a study, as well as welfare, justice and rights considerations. In making this argument, we explain the normative basis for consent waivers, and provide a starting place for further discussion about the precise conditions in which a given study can be said to advance the public interest. Ipsos MORI study found that: … the public would be broadly happy with administrative data linking for research projects provided Those projects have social value, broadly defined. Data are de-identified. Data are kept secure. Businesses are not able to access the data for profit. (shrink)

In the original publication of this article, the Table 1 has been published in a low resolution. Now a larger version of Table 1 is published in this correction. The publisher apologizes for the error made during production.

Machine behavior that is based on learning algorithms can be significantly influenced by the exposure to data of different qualities. Up to now, those qualities are solely measured in technical terms, but not in ethical ones, despite the significant role of training and annotation data in supervised machine learning. This is the first study to fill this gap by describing new dimensions of data quality for supervised machine learning applications. Based on the rationale that different social and psychological backgrounds of (...) individuals correlate in practice with different modes of human–computer-interaction, the paper describes from an ethical perspective how varying qualities of behavioral data that individuals leave behind while using digital technologies have socially relevant ramification for the development of machine learning applications. The specific objective of this study is to describe how training data can be selected according to ethical assessments of the behavior it originates from, establishing an innovative filter regime to transition from the big data rationale n = all to a more selective way of processing data for training sets in machine learning. The overarching aim of this research is to promote methods for achieving beneficial machine learning applications that could be widely useful for industry as well as academia. (shrink)

For people to contribute to discourse, they must do more than utter the right sentence at the right time. The basic requirement is that they add to their common ground in an orderly way. To do this, we argue, they try to establish for each utterance the mutual belief that the addressees have understood what the speaker meant well enough for current purposes. This is accomplished by the collective actions of the current contributor and his or her partners, and these (...) result in units of conversation called contributions. We present a model of contributions and show how it accounts for a variety of features of everyday conversations. (shrink)

Coronary by-pass surgery has been performed in hundreds of thousands of patients in the last 15 years with a high standard of technical and surgical perfection. The indications for this kind of surgery, however, are still controversial because in spite of many retrospective and several prospective studies it cannot be proven convincingly that in a given patient this surgical procedure will prolong life or prevent myocardial infarction. The present attempt to analyze the causes for this controversy shows that the main (...) reasons for this uncertainty lie: (a) in the enigma of the underlying (ischemic heart) disease itself, which is supposed to lend itself to surgical therapy; (b) in the inadequate methods available up until now for characterizing the state of the disease, its probable course, and its pathogenesis; and (c) in the professional group interests which have a momentum of their own. (shrink)

Due to the technology of machine learning, new breakthroughs are currently being achieved with constant regularity. By using machine learning techniques, computer applications can be developed and used to solve tasks that have hitherto been assumed not to be solvable by computers. If these achievements consider applications that collect and process personal data, this is typically perceived as a threat to information privacy. This paper aims to discuss applications from both fields of personality and image analysis. These applications are often (...) criticized by reference to the protection of privacy. This paper critically questions this approach. Instead of solely using the concept of privacy to address the risks of machine learning, it is increasingly necessary to consider and implement ethical anti-discrimination concepts, too. In many ways, informational privacy requires individual information control. However, not least because of machine learning technologies, information control has become obsolete. Hence, societies need stronger anti-discrimination tenets to counteract the risks of machine learning. (shrink)

P. Clodius’ lex frumentaria of 58 BC tends to be viewed as a populist law that greatly exacerbated, if not largely created, the problems associated with the provision of grain in the 50s. Yet the law was intended to alleviate the high costs due to increased demand created by Cato’s lex frumentaria of 62. Moreover, it was not conceived as a one-shot, populist solution, but was part of a larger strategy aimed at mitigating the problem of supply. In ways hitherto (...) unrealised, Clodius’ annexation of Cyprus and the consular exemption of Delos from taxation formed part of a wider debate on tackling the root cause of high costs and unreliable supply: the resurgence of piracy in the eastern Mediterranean. (shrink)

The COVID-19 pandemic has both exposed and created deep rifts in society. It has thrust us into deep ethical thinking to help justify the difficult decisions many will be called upon to make and to protect from decisions that lack ethical underpinnings. This paper aims to highlight ethical issues in six different areas of life highlighting the enormity of the task we are faced with globally. In the context of COVID-19, we consider health inequity, dilemmas in triage and allocation of (...) scarce resources, ethical issues associated with research, ethical considerations relating to tracing apps, and exit strategies such as immunity passports and COVID-19 vaccines. Finally, we consider environmental issues in light of COVID-19. The paper also offers some ethical reflection on these areas as many parts of the world contemplate the recovery phase. (shrink)

High degrees of uncertainty and a lack of effective therapeutic treatments have characterized the COVID-19 pandemic and the provision of drug products outside research settings has been controversial. International guidelines for providing patients with experimental interventions to treat infectious diseases outside of clinical trials exist but it is unclear if or how they should apply in settings where clinical trials and research are strongly regulated. We propose the Professional Oversight of Emergency-Use Interventions and Monitoring System as an alternative pathway based (...) on guidance developed for the ethical provision of experimental interventions to treat COVID-19 in Singapore. We support our proposal with justifications that establish moral duties for physicians to record outcomes data and for institutions to establish monitoring systems for reporting information on safety and effectiveness to the relevant authorities. Institutions also have a duty to support generation of evidence for what constitutes good clinical practice and so should ensure the unproven intervention is made the subject of research studies that can contribute to generalizable knowledge as soon as practical and that physicians remain committed to supporting learning health systems. We outline key differences between POEIMS and other pathways for the provision of experimental interventions in public health emergencies. (shrink)

Various geoengineering technologies that would deliberately alter the climate system have been proposed as a way to alleviate risks of global warming. Technologies that would shield incoming sunlight to cool the planet, so called solar radiation management, are particularly controversial. Considering insights from social studies of simulation modeling and research on expectations in science and technology, I argue that climate modeling has a central role in producing visions of SRM. I draw upon an empirical analysis of scientific research on SRM (...) to examine how a creative play with technological ideas becomes possible through climate modeling. This enables scientists to project and study environmental impacts of speculative SRM methods in virtual experiments and to develop and refine ideas for adjusting sunlight. Hence, while climate models are used to improve scientific understandings of climate system behavior and to anticipate possible environmental impacts of SRM, they also become inventive tools, allowing scientists to envision novel ways of climate control and optimization. Given the importance of simulation studies to knowledge production on SRM, I critically reflect on the challenges that arise when visions about an engineered climate future are first and foremost produced in climate simulations. (shrink)

Kants Religionsphilosophie by Ch. A. Thilo. This book is a reproduction of the original book published in 1905 and may have some imperfections such as marks or hand-written notes.

The predominant view is that a study using health data is observational research and should require individual consent unless it can be shown that gaining consent is impractical. But recent arguments have been made that citizens have an ethical obligation to share their health information for research purposes. In our view, this obligation is sufficient ground to expand the circumstances where secondary use research with identifiable health information is permitted without explicit subject consent. As such, for some studies the Institutional (...) Review Board/Research Ethics Committee review process should not assess the practicality of gaining consent for data use. Instead the review process should focus on assessing the public good of the research, public engagement and transparency. (shrink)