Suchergebnisse

Die Gesellschaft erlebt sich in einem Krisenmodus, ausgelöst durch eine Vielzahl ökologischer, sozialer und politischer Fehlentwicklungen. Was treibt diese an und wie können sie abgewendet werden? Bei der Beantwortung dieser Fragen wendet sich der Sozialwissenschaftler und Ethiker Thilo Hagendorff einem zentralen und dennoch kaum beachteten Aspekt zu: dem menschlichen Verhältnis zu Tieren. Denn gerade die industrielle Nutzung und Tötung von Tieren hat versteckte, aber weitreichende Implikationen für alle der genannten Fehlentwicklungen. Hagendorff zeigt die ideologischen wie psychologischen Mechanismen auf, die nicht nur zur Akzeptanz und Unterstützung von Gewalt gegenüber Tieren, sondern auch gegenüber Menschen führen. Mit Rückgriff auf zahlreiche wissenschaftliche Erkenntnisse macht er deutlich, dass die Abwendung aktueller Krisen untrennbar mit einem veränderten Umgang mit Tieren verknüpft ist. Eine von Frieden und gegenseitigem Respekt geprägte Gesellschaft ist ohne die Beendigung der globalen Tierindustrie nicht denkbar.

Digital media users have less and less control over their data and information. Hackers, IT companies, intelligence services, data brokers, and tech developers, as well as the emerging 'internet of things', big data, and untransparent algorithms are fast turning the idea of being able to control one's own data into a fiction. The boundaries between intimate, private, public and institutional data - information contexts previously kept separate from one another - are becoming increasingly blurred. This is Thilo Hagendorff's starting point, from which he introduces innovative strategies for media use which anticipate or even rely on this future loss of control - and which don't rely on vulnerable privacy norms and unreliable data protection technologies.. - Nutzerinnen und Nutzer digitaler Medien haben immer weniger Möglichkeiten der Daten- und Informationskontrolle. Hacker, IT-Unternehmen, Geheimdienste, Datenbroker, Technikentwickler etc., aber auch das aufkommende Internet der Dinge, Big Data oder intransparente Algorithmen treiben den Kontrollverlust voran. Vormals voneinander separierte Informationskontexte - Intimes, Privates, Öffentliches, Institutionelles etc. - gehen allmählich ineinander über. Hier setzt Thilo Hagendorff an: Er stellt innovative Mediennutzungsstrategien vor, die den Kontrollverlust erwarten oder sogar vielmehr fest mit ihm rechnen - und sich nicht auf verletzliche Privatheitsnormen oder unzuverlässige Datenschutztechnologien verlassen.

Der Zusammenhang von Sozialkritik und sozialer Steuerung ist in den Sozialwissenschaften bislang kaum untersucht worden. Der stillschweigende Anspruch einer engagierten Sozialphilosophie - die Verbesserung gesellschaftlicher Verhältnisse - kann jedoch nur über zielgerichtete Veränderungsintentionen, also über soziale Steuerungsarrangements, umgesetzt werden.Wie aber können diese aussehen? Thilo Hagendorff gibt hierauf Antworten. Seine Studie regt Modernisierungsschritte des methodischen Apparats der Sozialkritik ebenso an wie Effizienzsteigerungen sozialkritischer Diskurse.

AbstractMany 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.

AbstractAI ethics is deemed to be an essential ingredient in the quest for trustworthy AI. Hence, demands for implementing AI ethics and ethicists into AI organizations, especially corporations, are ubiquitous. However, the assumption that AI ethicists have particular epistemological advantages compared to non-ethicists as well as the idea that AI ethics automatically decreases the likelihood of unethical outcomes are both flawed. Therefore, this comment lists risks that either originate from AI ethicists themselves or from the consequences their embedding in AI organizations has. The compilation of risks comprises psychological considerations concerning the cognitive biases of AI ethicists themselves as well as biased reactions to their work, subject-specific and knowledge constraints AI ethicists often succumb to, negative side effects of ethics audits for AI applications, and many more. Ultimately, the aim of this comment is not to diminish or deny the importance of the discipline of AI ethics, but rather to increase its capacities for self-reflection and, ultimately, effectiveness.

AbstractThis paper critically discusses blind spots in AI ethics. AI ethics discourses typically stick to a certain set of topics concerning principles evolving mainly around explainability, fairness, and privacy. All these principles can be framed in a way that enables their operationalization by technical means. However, this requires stripping down the multidimensionality of very complex social constructs to something that is idealized, measurable, and calculable. Consequently, rather conservative, mainstream notions of the mentioned principles are conveyed, whereas critical research, alternative perspectives, and non-ideal approaches are largely neglected. Hence, one part of the paper considers specific blind spots regarding the very topics AI ethics focusses on. The other part, then, critically discusses blind spots regarding to topics that hold significant ethical importance but are hardly or not discussed at all in AI ethics. Here, the paper focuses on negative externalities of AI systems, exemplarily discussing the casualization of clickwork, AI ethics' strict anthropocentrism, and AI's environmental impact. Ultimately, the paper is intended to be a critical commentary on the ongoing development of the field of AI ethics. It makes the case for a rediscovery of the strength of ethics in the AI field, namely its sensitivity to suffering and harms that are caused by and connected to AI technologies.

Der Zusammenhang von Sozialkritik und sozialer Steuerung ist in den Sozialwissenschaften bislang kaum untersucht worden. Der stillschweigende Anspruch einer engagierten Sozialphilosophie - die Verbesserung gesellschaftlicher Verhältnisse - kann jedoch nur über zielgerichtete Veränderungsintentionen, also über soziale Steuerungsarrangements, umgesetzt werden. Wie aber können diese aussehen? Thilo Hagendorff gibt hierauf Antworten. Seine Studie regt Modernisierungsschritte des methodischen Apparats der Sozialkritik ebenso an wie Effizienzsteigerungen sozialkritischer Diskurse. The relation bet

Long description: Der Zusammenhang von Sozialkritik und sozialer Steuerung ist in den Sozialwissenschaften bislang kaum untersucht worden. Der stillschweigende Anspruch einer engagierten Sozialphilosophie - die Verbesserung gesellschaftlicher Verhältnisse - kann jedoch nur über zielgerichtete Veränderungsintentionen, also über soziale Steuerungsarrangements, umgesetzt werden. Wie aber können diese aussehen? Thilo Hagendorff gibt hierauf Antworten. Seine Studie regt Modernisierungsschritte des methodischen Apparats der Sozialkritik ebenso an wie Effizienzsteigerungen sozialkritischer Diskurse.

AbstractFairness 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.

AbstractRecent progress in large language models has led to applications that can (at least) simulate possession of full moral agency due to their capacity to report context-sensitive moral assessments in open-domain conversations. However, automating moral decision-making faces several methodological as well as ethical challenges. They arise in the fields of bias mitigation, missing ground truth for moral "correctness", effects of bounded ethicality in machines, changes in moral norms over time, risks of using morally informed AI systems as actual advice, as well as societal implications an increasing importance of algorithmic moral decision-making would have. This paper comments on all these challenges and provides critical considerations for future research on full artificial moral agency. Importantly, some of the adduced challenges can be met by more careful technology design, but others necessarily require engagement with core problems of meta-ethics.

AbstractThe elimination of biases in artificial intelligence (AI) applications—for example biases based on race or gender—is a high priority in AI ethics. So far, however, efforts to eliminate bias have all been anthropocentric. Biases against nonhuman animals have not been considered, despite the influence AI systems can have on normalizing, increasing, or reducing the violence that is inflicted on animals, especially on farmed animals. Hence, in 2022, we published a paper in AI and Ethics in which we empirically investigated various examples of image recognition, word embedding, and language models, with the aim of testing whether they perpetuate speciesist biases. A critical response has appeared in AI and Ethics, accusing us of drawing upon theological arguments, having a naive anti-speciesist mindset, and making mistakes in our empirical analyses. We show that these claims are misleading.