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Germany's future contribution to European and allied security depends on the Bundeswehr's ability to harness emerging and disruptive technologies (EDTs) such as artificial intelligence, 5G/6G cellular network technology, Low Earth Orbit (LEO) satellite connectivity, and quantum communications and computation. Even amidst Russia's war of aggression against Ukraine, Germany continues to be mired in siloed conceptual, institutional, and ethical thinking that results in disconnections between the military and the technology sector, and even between ­Germany and its allies. The Zeitenwende should catalyze not only a defense budgetary increase but a reconciliation between ethics and military requirements regarding EDTs if Germany is to look beyond immediate needs and ensure the Bundeswehr's future ­operational readiness.

The COVID-era public and private investment influx into Germany's digital technology R&D is reversing amid inflation, fiscal consolidation, and geopolitical pressures coming from the Zeitenwende. Germany's future in an EU that is among the top-tier technology powers requires a profound and rapid transition of the country's R&D strengths into data-intensive, systems-centric areas of IoT and deep technology that are linked to the domestic manufacturing base. New policy approaches in three areas - money, markets, and minds - are needed. New technologies such as robotics, artificial intelligence (AI), advanced material science, biotech, and quantum computing tend to have broad general-purpose applications. But uncoordinated funding vehicles, universities' civil clauses, and restrictive visa and onboarding guidelines for skilled foreign workers slow innovation in these sectors and hamper German techno-geopolitical competitiveness.

The automation bias describes the phenomenon, proven in behavioural psychology, that people place excessive trust in the decision suggestions of machines. The law currently sees a dichotomy - and covers only fully automated decisions, and not those involving human decision makers at any stage of the process. However, the widespread use of such systems, for example to inform decisions in education or benefits administration, creates a leverage effect and increases the number of people affected. Particularly in environments where people routinely have to make a large number of similar decisions, the risk of automation bias increases. As an example, automated decisions providing suggestions for job placements illustrate the particular challenges of decision support systems in the public sector. So far, the risks have not been sufficiently addressed in legislation, as the analysis of the GDPR and the draft Artificial Intelligence Act show. I argue for the need for regulation and present initial approaches.

Recently, a host of propositions for guidelines for the ethical development and use of artificial intelligence (AI) has been published. This body of work contains timely contributions for sensitizing developers to the ethical and societal implications of their work. However, a sustained embedding of ethics in largely algorithm-based technology development, research and studies requires a precise framing of the origins of the new vulnerabilities created. Recently, scholars have been referring to ethics associated with technology that is in some way "opaque" to at least part of its associated stakeholders. This "opacity" can take several forms which will be discussed in this paper. There are various ways in which such an opacity can create vulnerabilities and, hence, relevant ethical, societal, epistemic and regulatory challenges. This paper provides a non-exhaustive list of examples in healthcare that call for educational resources and consideration in development processes that try to reveal and counter these opacities.

"Industry 4.0, Internet of Things and Artificial Intelligence have become familiar sayings in public discussions over the past few years. In numerous media, the current discussion revolves around the notion of whether, after the introduction of the PC and the development of the Internet, the next technological revolution is imminent in the form of self-regulating and interconnected systems and machines. A question that is central to the discussion here is the following: Will self-regulating machines almost completely replace human manpower in the future? With this in mind, the discussion is primarily about how the new technologies will affect professional and private life. The aim of this article is to provide a concise stocktaking of the current economic literature on the topic of technology and the labour market." (Text excerpt IAB-Doku)

Eine Vielzahl von AI-Forschern betrachtet erfolgreiche AI-Programme als Theorien über die modellierten Phänomene. Der vorliegende Beitrag untersucht, inwieweit diese Auffassung gerechtfertigt ist. Während bei der Umsetzung einer vorliegenden Theorie in ein Computerprogramm dem Programm i.a. ein Theoriestatus nicht abzuleugnen ist, können nicht alle erfolgreichen AI-Programme auch als Theorien betrachtet werden. Da Theorien erklärungskräftig sind und Erklärungen
nur mit Hilfe von Gesetzen möglich sind, müssen "theoretische" Programme gesetzesartige Verallgemeinerungen enthalten. Sich daraus ergebende Probleme werden am Schluß dieses Aufsatzes diskutiert.

This paper argues that the hype around 'ethics' as panacea for remedying algorithmic discrimination is a smokescreen for carrying on with business as usual. First, it analyses how the current discourses around digital innovation and algorithmic technologies (including artificial intelligence or AI), newly emerging technology policy and governmental funding patterns as well as global industry developments are currently re-configured around 'ethical' considerations. Here, the paper shows how this phenomenon can be broken down into policy approaches and technological approaches. Second, it sets out to provide three pillars for a sociological framework that can help reconceptualize the algorithmic harm and discrimination as an issue of social inequality, rather than ethics. Here, it builds on works on data classification, human agency in design and intersectional inequality. To conclude, the paper suggests three pragmatic steps that should be taken in order to center social justice in technology policy and computer science education.

Web search engines influence perception of social reality by filtering and ranking information. However, their outputs are often subjected to bias that can lead to skewed representation of subjects such as professional occupations or gender. In our paper, we use a mixed-method approach to investigate presence of race and gender bias in representation of artificial intelligence (AI) in image search results coming from six different search engines. Our findings show that search engines prioritize anthropomorphic images of AI that portray it as white, whereas non-white images of AI are present only in non-Western search engines. By contrast, gender representation of AI is more diverse and less skewed towards a specific gender that can be attributed to higher awareness about gender bias in search outputs. Our observations indicate both the need and the possibility for addressing bias in representation of societally relevant subjects, such as technological innovation, and emphasize the importance of designing new approaches for detecting bias in information retrieval systems.

Artificial intelligence (AI) is increasingly becoming part of many processes in the news media industry. It is seen as highly relevant for speeding up workflows and content personalisation. At the same time, the introduction of AI is having a strong impact on workflows, trust, roles and responsibilities of the professional newsworkers, who are consequently often reluctant to collaborate with the new, intelligent technology. These case study analyses investigate with an HMC (human-machine communication) lens how to create conditions for supporting and improving professional-AI collaboration. Besides identifying relevant drivers, existing overlaps of human-machine collaboration theory and other theoretical fields as well as practical measures for management and AI providers to promote professional-AI collaboration in the newsroom will be uncovered. The results highlight (1) comprehensibility of the AI, (2) the AI's benefits and (3) knowledge sharing as most important drivers. The study introduces furthermore concrete indications for future research.

Artificial intelligence (AI) strategies are exhibiting a shift of perspectives, focusing more intensively on a more human-centric view. New conceptualizations of AI literacy (AIL) are being presented, summarizing the competencies human users need to successfully interact with AI-based systems. However, these conceptualizations lack practical relevance. In view of the rapid pace of technological development, this contribution addresses the urgent need to bridge the gap between theoretical concepts of AIL and practical requirements of working environments. It transfers current conceptualizations and new principles of a more human-centered perspective on AI into professional working environments. From a psychological perspective, the project focuses on emotional-motivational, eudaimonic, and social aspects. Methodologically, the project presented develops AI testbeds in virtual reality to realize literally graspable interactions with AI-based technologies in the actual work environment. Overall, the project aims to increase the competencies and the willingness to successfully master the challenges of the digitalized world of work.

This paper centrally asks for the ways in which ubiquitous, ever new digital technologies of 'our' everyday lives transform learning at the digital human-machine interface from the perspective of feminist science and technology studies. How to account for emerging forms of interwoven human and machine learning? Suggesting the term of learning cultures in approaching this question, the paper emphasizes an understanding of learning not as a proficiency of an entity embodying either natural or artificial intelligence, but rather as a culturally situated and materially enacted process. In so doing, the paper brings together recent impulses that suggest a re-conceptualization of learning, e.g. through the notion of "machine learners" (Mackenzie 2017) or that of "posthuman learning (Hasse 2018)". Reading these insights together, I will finally suggest an account of becoming responsible for learning cultures of digital technologies through a reconsidered notion of interwoven human/machine learning.

In this paper, we present our ideas on how to best support researchers in every phase of the research process when dealing with their research data. We propose a Research Data Portal as the central data infrastructure. With the help of this portal, a researcher can easily manage and update his or her research data, share it with collaborators, and reach out to the public. We further propose a Citizen Science Portal, which includes some new and innovative concepts and methods. In this portal, Citizen Science and Auto Science concepts are applied, and support to bring together the best of both worlds is provided. Citizen Science promises to entail the individual (scientists and hobby scientists) to help with research. Auto Science is meant to help analyze research data, e.g., to help publish the data and to help improve its quality, by applying methods from artificial intelligence.

This paper will first investigate possible contributions that an AI-based detector for deepfakes could make to the challenge of responding to disinformation as a threat to democracy. Second, this paper will also investigate the implications of such a tool - which was developed, among other reasons, for security purposes - for the emerging European discourse on digital sovereignty in a global environment. While disinformation is surely not a new topic, recent technological developments relating to AI-generated deepfakes have increased the manipulative potential of video and audio-based contents spread online, making it a specific but important current challenge in the global and interconnected information context.

Künstliche Intelligenz (KI) ändert unser Leben und unser Zusammenleben zunehmend. Bei KI handelt es sich um Technologien im Werden. Sie werden durch Leitlinien in der Form von Recht oder Strategien gelenkt und beeinflusst. Der Beitrag beleuchtet KI-Systeme in der öffentlichen Verwaltung und wirft die Frage auf, welche Leitlinien es für künstliche Intelligenz bereits gibt und welche Tendenzen sich abzeichnen. Nach einer Definition von künstlicher Intelligenz und einigen Beispielen aus dem Bereich von Regierung und Verwaltung macht der Beitrag Ethik und Politik als mögliche Bezugspunkte für Leitlinien aus. Davon ausgehend werden Recht, Technik, Organisation, Strategie und Leitbilder als Einflussmöglichkeiten vorgestellt, wobei aktuelle Trends geschildert werden. Der Beitrag schließt mit einer Forderung nach interdisziplinärer Forschung und maßvoller Regulierung der Technik, um ihr positives Potenzial auszuschöpfen.