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)

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)

Dance provides natural conditions for studying relationships between coordination patterns and human experience. von Zimmermann and colleagues investigate whether relatively simple or more complex forms of movement coordination are related to pro‐social experiences during group dance. They find that pro‐social experience depends on the degree to which movement patterns are distributed and diversified, but not the degree to which movement patterns are simply unitary.

Die MISCELLANEA MEDIAEVALIA präsentieren seit ihrer Gründung durch Paul Wilpert im Jahre 1962 Arbeiten des Thomas-Instituts der Universität zu Köln. Das Kernstück der Publikationsreihe bilden die Akten der im zweijährigen Rhythmus stattfindenden Kölner Mediaevistentagungen, die vor über 50 Jahren von Josef Koch, dem Gründungsdirektor des Instituts, ins Leben gerufen wurden. Der interdisziplinäre Charakter dieser Kongresse prägt auch die Tagungsakten: Die MISCELLANEA MEDIAEVALIA versammeln Beiträge aus allen mediävistischen Disziplinen - die mittelalterliche Geschichte, die Philosophie, die Theologie sowie die Kunst- und Literaturwissenschaften (...) sind Teile einer Gesamtbetrachtung des Mittelalters. (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)

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)

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)

Die MISCELLANEA MEDIAEVALIA präsentieren seit ihrer Gründung durch Paul Wilpert im Jahre 1962 Arbeiten des Thomas-Instituts der Universität zu Köln. Das Kernstück der Publikationsreihe bilden die Akten der im zweijährigen Rhythmus stattfindenden Kölner Mediaevistentagungen, die vor über 50 Jahren von Josef Koch, dem Gründungsdirektor des Instituts, ins Leben gerufen wurden. Der interdisziplinäre Charakter dieser Kongresse prägt auch die Tagungsakten: Die MISCELLANEA MEDIAEVALIA versammeln Beiträge aus allen mediävistischen Disziplinen - die mittelalterliche Geschichte, die Philosophie, die Theologie sowie die Kunst- und Literaturwissenschaften (...) sind Teile einer Gesamtbetrachtung des Mittelalters. (shrink)

What is the fruit of a searching dialogue between faith and reason? This book collects theological and philosophical perspectives on the richness of the faith-reason dialogue, including examples from literature, continental and analytic philosophy, worship and liturgy, and radical approaches to issues of racism and prejudice. The authors strongly resist the temptations to either disregard the faith-reason dialogue or take it for granted. Through their explorations and reflections they open up new vistas and horizons on a topic more necessary than (...) ever. (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)

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)

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.

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.

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)

Fairness in machine learning (ML) is an ever-growing field of research due to the manifold potential for harm from algorithmic discrimination. To prevent such harm, a large body of literature develops new approaches to quantify fairness. Here, we investigate how one can divert the quantification of fairness by describing a practice we call “fairness hacking” for the purpose of shrouding unfairness in algorithms. This impacts end-users who rely on learning algorithms, as well as the broader community interested in fair AI (...) practices. We introduce two different categories of fairness hacking in reference to the established concept of p-hacking. The first category, intra-metric fairness hacking, describes the misuse of a particular metric by adding or removing sensitive attributes from the analysis. In this context, countermeasures that have been developed to prevent or reduce p-hacking can be applied to similarly prevent or reduce fairness hacking. The second category of fairness hacking is inter-metric fairness hacking. Inter-metric fairness hacking is the search for a specific fair metric with given attributes. We argue that countermeasures to prevent or reduce inter-metric fairness hacking are still in their infancy. Finally, we demonstrate both types of fairness hacking using real datasets. Our paper intends to serve as a guidance for discussions within the fair ML community to prevent or reduce the misuse of fairness metrics, and thus reduce overall harm from ML applications. (shrink)