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AbstractMassive efforts are made to reduce biases in both data and algorithms to render AI applications fair. These efforts are propelled by various high-profile cases where biased algorithmic decision-making caused harm to women, people of color, minorities, etc. However, the AI fairness field still succumbs to a blind spot, namely its insensitivity to discrimination against animals. This paper is a critical comment on current fairness research in AI. It is the first to describe the 'speciesist bias' and investigate it in several different AI systems by reflecting on the problem via a normative analysis and by probing, in several case studies, image recognition, word embedding, and language models with established methods for bias detection. We claim that animals matter morally and that discriminating against them is unethical. Furthermore, we provide evidence for speciesist biases in all the mentioned areas of AI. We find that speciesist biases are solidified by many mainstream AI applications, especially in the fields of computer vision as well as natural language processing. In both cases, this occurs because the models are trained on datasets in which speciesist patterns prevail. Therefore, AI technologies currently play a significant role in perpetuating and normalizing violence against animals. To change this, AI fairness frameworks must widen their scope and include mitigation measures for speciesist biases. This paper addresses the AI community in this regard and stresses the influence AI systems can have on either increasing or reducing the violence that is inflicted on animals, especially on farmed animals.

In Reaktion auf die Enthüllungen Edward Snowdens und die dadurch aktualisierte Krise der Privatheit tritt die Reterritorialisierung des Digitalen als Reaktionsweise zeitgenössischer Demokratien auf den Plan. Verwerfungen im Zuge zunehmend globalisierter Datenströme sollen nach altbekanntem Muster der territorialen und nationalenContainergesellschaften gekittet werden. Reterritorialisierung adressiert dabei einen digital induzierten Wandel und versucht, Antworten zu geben auf Fragen der gegenwärtigen Neukonfiguration des Verhältnisses von Demokratie und Gesellschaft. Die Kartografie der dynamischen Arena um das umstrittene Konzept Privatheit liefertempirische Erkenntnisse zu einem der meistdiskutierten sozialen Ordnungsmechanismen und zeigt, wie die politische Suche nach Lösungen angesichts der tektonischen Veschiebungen der Digitalisierung mit bestehenden Institutionen, Routinen und Ressourcen verbunden bleibt. Die Publikation präsentiert Ergebnisse des BMBF-Projektes "Kartografie und Analyse der Privacy-Arena", an dem die Disziplinen Soziologie, Rechtswissenschaft und Philosophie/Ethik beteiligt sind.

In Reaktion auf die Enthüllungen Edward Snowdens und die dadurch aktualisierte Krise der Privatheit tritt die Reterritorialisierung des Digitalen als Reaktionsweise zeitgenössischer Demokratien auf den Plan. Verwerfungen im Zuge zunehmend globalisierter Datenströme sollen nach altbekanntem Muster der territorialen und nationalen Containergesellschaften gekittet werden. Reterritorialisierung adressiert dabei einen digital induzierten Wandel und versucht, Antworten zu geben auf Fragen der gegenwärtigen Neukonfiguration des Verhältnisses von Demokratie und Gesellschaft. Die Kartografie der dynamischen Arena um das umstrittene Konzept Privatheit liefert empirische Erkenntnisse zu einem der meistdiskutierten sozialen Ordnungsmechanismen und zeigt, wie die politische Suche nach Lösungen angesichts der tektonischen Verschiebungen der Digitalisierung mit bestehenden Institutionen, Routinen und Ressourcen verbunden bleibt. Die Publikation präsentiert Ergebnisse des BMBF-Projektes "Kartografie und Analyse der Privacy-Arena", an dem die Disziplinen Soziologie, Rechtswissenschaft und Philosophie/Ethik beteiligt sind. ; Also available via the publisher: https://doi.org/10.19211/KUP9783862191079

Interaktive Medienformen - Internet der Zukunft - Internet der Dinge, IoT - Interaktion

Artificial intelligence (AI) is a technology which is increasingly being utilised in society and the economy worldwide, and its implementation is planned to become more prevalent in coming years. AI is increasingly being embedded in our lives, supplementing our pervasive use of digital technologies. But this is being accompanied by disquiet over problematic and dangerous implementations of AI, or indeed, even AI itself deciding to do dangerous and problematic actions, especially in fields such as the military, medicine and criminal justice. These developments have led to concerns about whether and how AI systems adhere, and will adhere to ethical standards. These concerns have stimulated a global conversation on AI ethics, and have resulted in various actors from different countries and sectors issuing ethics and governance initiatives and guidelines for AI. Such developments form the basis for our research in this report, combining our international and interdisciplinary expertise to give an insight into what is happening in Australia, China, Europe, India and the US.

. ; Policy Paper (Forum Privatheit und selbstbestimmtes Leben in der digitalen Welt)

This report is a methodological reflection on Z-Inspection. Z-Inspection is a holistic process used to evaluate the trustworthiness of AI-based technologies at different stages of the AI lifecycle. It focuses, in particular, on the identification and discussion of ethical issues and tensions through the elaboration of socio-technical scenarios. It uses the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI. This report illustrates for both AI researchers and AI practitioners how the EU HLEG guidelines for trustworthy AI can be applied in practice. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of AI systems in healthcare. We also share key recommendations and practical suggestions on how to ensure a rigorous trustworthy AI assessment throughout the life-cycle of an AI system.

This paper documents how an ethically aligned co-design methodology ensures trustworthiness in the early design phase of an artificial intelligence (AI) system component for healthcare. The system explains decisions made by deep learning networks analyzing images of skin lesions. The co-design of trustworthy AI developed here used a holistic approach rather than a static ethical checklist and required a multidisciplinary team of experts working with the AI designers and their managers. Ethical, legal, and technical issues potentially arising from the future use of the AI system were investigated. This paper is a first report on co-designing in the early design phase. Our results can also serve as guidance for other early-phase AI-similar tool developments.

Artificial Intelligence (AI) has the potential to greatly improve the delivery of healthcare and other services that advance population health and wellbeing. However, the use of AI in healthcare also brings potential risks that may cause unintended harm. To guide future developments in AI, the High-Level Expert Group on AI set up by the European Commission (EC), recently published ethics guidelines for what it terms "trustworthy" AI. These guidelines are aimed at a variety of stakeholders, especially guiding practitioners toward more ethical and more robust applications of AI. In line with efforts of the EC, AI ethics scholarship focuses increasingly on converting abstract principles into actionable recommendations. However, the interpretation, relevance, and implementation of trustworthy AI depend on the domain and the context in which the AI system is used. The main contribution of this paper is to demonstrate how to use the general AI HLEG trustworthy AI guidelines in practice in the healthcare domain. To this end, we present a best practice of assessing the use of machine learning as a supportive tool to recognize cardiac arrest in emergency calls. The AI system under assessment is currently in use in the city of Copenhagen in Denmark. The assessment is accomplished by an independent team composed of philosophers, policy makers, social scientists, technical, legal, and medical experts. By leveraging an interdisciplinary team, we aim to expose the complex trade-offs and the necessity for such thorough human review when tackling socio-technical applications of AI in healthcare. For the assessment, we use a process to assess trustworthy AI, called 1Z-Inspection® to identify specific challenges and potential ethical trade-offs when we consider AI in practice.