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German and European migration policy operates in permanent crisis mode. Sudden increases in irregular immigration create a sense of loss of control, which is instrumentalised by populist forces. This has generated great interest in quantitative migration predictions. High expectations are placed in the AI-based tools currently under devel­op­ment for forecasting irregular migration. The potential applications of these tools are manifold. They range from managing and strengthening the EU's reception capacity and border protections to configuring humanitarian aid provision and longer-term planning of development programmes. There is a significant gap between the expectations placed in the new instruments and their practical utility. Technical limits exist, medium-term forecasts are methodologically implausible, and channels for feeding the results into political decision-making processes are lacking. The great demand for predictions is driven by the political functions of migration prediction, which include its uses in political communication, funding acquisition and legitimisation of political decisions. Investment in the quality of the underlying data will be more productive than developing a succession of new prediction tools. Funding for applications in emergency relief and development cooperation should be prioritised. Crisis early warning and risk analysis should also be strengthened and their networking improved.
Die deutsche und europäische Migrationspolitik befindet sich im permanenten Krisenmodus. Plötzliche Anstiege ungeregelter Zuwanderung nähren ein Gefühl von Kontrollverlust, das wiederum von populistischen Kräften instrumentalisiert wird. Daher hat die Politik großes Interesse an quantitativen Migrationsprognosen. Besondere Erwartungen wecken KI-gestützte Instrumente zur Vorhersage ungeregelter Wanderungsbewegungen, wie sie zurzeit entwickelt werden. Die Anwendungsfelder dieser Instrumente sind vielfältig. Sie reichen von einer Stärkung der Aufnahmekapazitäten in der EU über die präventive Verschärfung von Grenzschutzmaßnahmen und eine bedarfsgerechte Bereitstellung von Ressourcen in humanitären Krisen bis zur längerfristigen entwicklungspolitischen Programmplanung. Allerdings besteht eine deutliche Kluft zwischen den Erwartungen an die neuen Instrumente und ihrem praktischen Mehrwert. Zum einen sind die technischen Möglichkeiten begrenzt, und mittelfristige Vorhersagen zu ungeregelten Wanderungen sind methodisch kaum möglich. Zum anderen mangelt es an Verfahren, um die Ergebnisse in politische Entscheidungsprozesse einfließen zu lassen. Die hohe Nachfrage nach Prognosen erklärt sich aus den politischen Funktionen quantitativer Migrationsvorhersage - beispielsweise ihrem Potential für die politische Kommunikation, die Mitteleinwerbung und die Legitimierung politischer Entscheidungen. Investitionen in die Qualität der den Prognosen zugrunde liegenden Daten sind sinnvoller als die Entwicklung immer neuer Instrumente. Bei der Mittelvergabe für Prognosen sollten Anwendungen in der Nothilfe und der Entwicklungszusammenarbeit priorisiert werden. Zudem sollten die Krisenfrüherkennung und die Risikoanalyse gestärkt werden, und die beteiligten Akteure sollten sich besser vernetzen.
Deep learning has seen widespread application in many domains, mainly for its ability to learn data representations from raw input data. Nevertheless, its success has so far been coupled with the availability of large annotated (labelled) datasets. This is a requirement that is difficult to fulfil in several domains, such as in medical imaging. Annotation costs form a barrier in extending deep learning to clinically-relevant use cases. The labels associated with medical images are scarce, since the generation of expert annotations of multimodal patient data at scale is non-trivial, expensive, and time-consuming. This substantiates the need for algorithms that learn from the increasing amounts of unlabeled data. Self-supervised representation learning algorithms offer a pertinent solution, as they allow solving real-world (downstream) deep learning tasks with fewer annotations. Self-supervised approaches leverage unlabeled samples to acquire generic features about different concepts, enabling annotation-efficient downstream task solving subsequently.
Nevertheless, medical images present multiple unique and inherent challenges for existing self-supervised learning approaches, which we seek to address in this thesis: (i) medical images are multimodal, and their multiple modalities are heterogeneous in nature and imbalanced in quantities, e.g. MRI and CT; (ii) medical scans are multi-dimensional, often in 3D instead of 2D; (iii) disease patterns in medical scans are numerous and their incidence exhibits a long-tail distribution, so it is oftentimes essential to fuse knowledge from different data modalities, e.g. genomics or clinical data, to capture disease traits more comprehensively; (iv) Medical scans usually exhibit more uniform color density distributions, e.g. in dental X-Rays, than natural images. Our proposed self-supervised methods meet these challenges, besides significantly reducing the amounts of required annotations.
We evaluate our self-supervised methods on a wide array of medical imaging applications and tasks. Our experimental results demonstrate the obtained gains in both annotation-efficiency and performance; our proposed methods outperform many approaches from related literature. Additionally, in case of fusion with genetic modalities, our methods also allow for cross-modal interpretability. In this thesis, not only we show that self-supervised learning is capable of mitigating manual annotation costs, but also our proposed solutions demonstrate how to better utilize it in the medical imaging domain. Progress in self-supervised learning has the potential to extend deep learning algorithms application to clinical scenarios.
Die Nutzung von Informations- und Kommunikationstechnik (IKT), Fachverfahren und die Automatisierung von Prozessen verändern die Sachbearbeitung und Leistungserstellung in der Verwaltung und somit die Tätigkeiten, Arbeitsbedingungen und Personalstrukturen. Bei der Antragsbearbeitung und Bescheiderstellung in der Ordnungs- und Leistungsverwaltung erhält IKT nicht nur eine unterstützende, sondern zunehmend auch eine leitende oder entscheidende Rolle. Abhängig von der konkreten Ausgestaltung kann die fortschreitende Digitalisierung eine ganzheitliche Sachbearbeitung ermöglichen, aber auch einschränken. Insgesamt kann sie zu einer Neuordnung des Berufsfeldes öffentlicher Dienst führen.
Der Einsatz Künstlicher Intelligenz (KI) wird zunehmend relevant – sowohl in Berufen mit formalisierbaren Aufgaben als auch in Berufsfeldern, für deren Aufgaben Erfahrungswissen notwendig ist und situationsabhängig Entscheidungen getroffen werden, die mit folgenschweren Konsequenzen verbunden sein können. Um das Potenzial der Zusammenarbeit zwischen Mensch und KI auszuschöpfen, muss sich der Mensch entsprechend wappnen. Somit verändern sich die Kompetenzanforderungen an Mitarbeiter:innen auf allen Ebenen und an ihre Führungskräfte. Relevante Konzepte des lebenslangen Lernens und der betrieblichen Weiterbildung gewinnen durch den Einfluss der Technologie auch unter teilweise veränderten Lernbedingungen vermehrt an Bedeutung. Neben neuen technischen und Fachkompetenzen, sind für die Nutzung von und die Zusammenarbeit mit der neuen Technologie weitere Kompetenzen notwendig, um z. B. einschätzen zu können, wann die Arbeit der Maschine ethisch vertretbar, effektiv, verantwortungsvoll, fair, transparent und nachvollziehbar ist. Auch neue Tätigkeitsprofile entstehen und die beruflichen Rollen verändern sich entsprechend. Neben den Anforderungen, die die KI an Bildung und Kompetenzentwicklung stellt, wird sie weiterhin zunehmend zur Gestaltung von Lernumgebungen und für den Kompetenzaufbau im Beruf eingesetzt. Sie ist somit nicht nur der Auslöser von Veränderungen, sondern auch das Instrument, welches genutzt wird, um die Lehre zu unterstützen und individueller, abwechslungsreicher sowie zeit- und ortunabhängiger zu gestalten. Im Beitrag werden Chancen und Herausforderungen durch den Einsatz von KI für zwei Dimensionen diskutiert: die Transformationsprozesse in der Berufswelt und die Gestaltung von Lernprozessen.
Digitalisation in industry – also called “Industry 4.0” – is seen by numerous actors as an opportunity to reduce the environmental impact of the industrial sector. The scientific assessments of the effects of digitalisation in industry on environmental sustainability, however, are ambivalent. This cumulative dissertation uses three empirical studies to examine the expected and observed effects of digitalisation in industry on environmental sustainability. The aim of this dissertation is to identify opportunities and risks of digitalisation at different system levels and to derive options for action in politics and industry for a more sustainable design of digitalisation in industry. I use an interdisciplinary, socio-technical approach and look at selected countries of the Global South (Study 1) and the example of China (all studies). In the first study (section 2, joint work with Marcel Matthess), I use qualitative content analysis to examine digital and industrial policies from seven different countries in Africa and Asia for expectations regarding the impact of digitalisation on sustainability and compare these with the potentials of digitalisation for sustainability in the respective country contexts. The analysis reveals that the documents express a wide range of vague expectations that relate more to positive indirect impacts of information and communication technology (ICT) use, such as improved energy efficiency and resource management, and less to negative direct impacts of ICT, such as electricity consumption through ICT. In the second study (section 3, joint work with Marcel Matthess, Grischa Beier and Bing Xue), I conduct and analyse interviews with 18 industry representatives of the electronics industry from Europe, Japan and China on digitalisation measures in supply chains using qualitative content analysis. I find that while there are positive expectations regarding the effects of digital technologies on supply chain sustainability, their actual use and observable effects are still limited. Interview partners can only provide few examples from their own companies which show that sustainability goals have already been pursued through digitalisation of the supply chain or where sustainability effects, such as resource savings, have been demonstrably achieved. In the third study (section 4, joint work with Peter Neuhäusler, Melissa Dachrodt and Marcel Matthess), I conduct an econometric panel data analysis. I examine the relationship between the degree of Industry 4.0, energy consumption and energy intensity in ten manufacturing sectors in China between 2006 and 2019. The results suggest that overall, there is no significant relationship between the degree of Industry 4.0 and energy consumption or energy intensity in manufacturing sectors in China. However, differences can be found in subgroups of sectors. I find a negative correlation of Industry 4.0 and energy intensity in highly digitalised sectors, indicating an efficiency-enhancing effect of Industry 4.0 in these sectors. On the other hand, there is a positive correlation of Industry 4.0 and energy consumption for sectors with low energy consumption, which could be explained by the fact that digitalisation, such as the automation of previously mainly labour-intensive sectors, requires energy and also induces growth effects. In the discussion section (section 6) of this dissertation, I use the classification scheme of the three levels macro, meso and micro, as well as of direct and indirect environmental effects to classify the empirical observations into opportunities and risks, for example, with regard to the probability of rebound effects of digitalisation at the three levels. I link the investigated actor perspectives (policy makers, industry representatives), statistical data and additional literature across the system levels and consider political economy aspects to suggest fields of action for more sustainable (digitalised) industries. The dissertation thus makes two overarching contributions to the academic and societal discourse. First, my three empirical studies expand the limited state of research at the interface between digitalisation in industry and sustainability, especially by considering selected countries in the Global South and the example of China. Secondly, exploring the topic through data and methods from different disciplinary contexts and taking a socio-technical point of view, enables an analysis of (path) dependencies, uncertainties, and interactions in the socio-technical system across different system levels, which have often not been sufficiently considered in previous studies. The dissertation thus aims to create a scientifically and practically relevant knowledge basis for a value-guided, sustainability-oriented design of digitalisation in industry.
Für die Entwicklung professioneller Handlungskompetenzen angehender Lehrkräfte stellt die Unterrichtsreflexion ein wichtiges Instrument dar, um Theoriewissen und Praxiserfahrungen in Beziehung zu setzen. Die Auswertung von Unterrichtsreflexionen und eine entsprechende Rückmeldung stellt Forschende und Dozierende allerdings vor praktische wie theoretische Herausforderungen. Im Kontext der Forschung zu Künstlicher Intelligenz (KI) entwickelte Methoden bieten hier neue Potenziale. Der Beitrag stellt überblicksartig zwei Teilstudien vor, die mit Hilfe von KI-Methoden wie dem maschinellen Lernen untersuchen, inwieweit eine Auswertung von Unterrichtsreflexionen angehender Physiklehrkräfte auf Basis eines theoretisch abgeleiteten Reflexionsmodells und die automatisierte Rückmeldung hierzu möglich sind. Dabei wurden unterschiedliche Ansätze des maschinellen Lernens verwendet, um modellbasierte Klassifikation und Exploration von Themen in Unterrichtsreflexionen umzusetzen. Die Genauigkeit der Ergebnisse wurde vor allem durch sog. Große Sprachmodelle gesteigert, die auch den Transfer auf andere Standorte und Fächer ermöglichen. Für die fachdidaktische Forschung bedeuten sie jedoch wiederum neue Herausforderungen, wie etwa systematische Verzerrungen und Intransparenz von Entscheidungen. Dennoch empfehlen wir, die Potenziale der KI-basierten Methoden gründlicher zu erforschen und konsequent in der Praxis (etwa in Form von Webanwendungen) zu implementieren.
Modern datasets often exhibit diverse, feature-rich, unstructured data, and they are massive in size. This is the case of social networks, human genome, and e-commerce databases. As Artificial Intelligence (AI) systems are increasingly used to detect pattern in data and predict future outcome, there are growing concerns on their ability to process large amounts of data. Motivated by these concerns, we study the problem of designing AI systems that are scalable to very large and heterogeneous data-sets.
Many AI systems require to solve combinatorial optimization problems in their course of action. These optimization problems are typically NP-hard, and they may exhibit additional side constraints. However, the underlying objective functions often exhibit additional properties. These properties can be exploited to design suitable optimization algorithms. One of these properties is the well-studied notion of submodularity, which captures diminishing returns. Submodularity is often found in real-world applications. Furthermore, many relevant applications exhibit generalizations of this property.
In this thesis, we propose new scalable optimization algorithms for combinatorial problems with diminishing returns. Specifically, we focus on three problems, the Maximum Entropy Sampling problem, Video Summarization, and Feature Selection. For each problem, we propose new algorithms that work at scale. These algorithms are based on a variety of techniques, such as forward step-wise selection and adaptive sampling. Our proposed algorithms yield strong approximation guarantees, and the perform well experimentally.
We first study the Maximum Entropy Sampling problem. This problem consists of selecting a subset of random variables from a larger set, that maximize the entropy. By using diminishing return properties, we develop a simple forward step-wise selection optimization algorithm for this problem. Then, we study the problem of selecting a subset of frames, that represent a given video. Again, this problem corresponds to a submodular maximization problem. We provide a new adaptive sampling algorithm for this problem, suitable to handle the complex side constraints imposed by the application. We conclude by studying Feature Selection. In this case, the underlying objective functions generalize the notion of submodularity. We provide a new adaptive sequencing algorithm for this problem, based on the Orthogonal Matching Pursuit paradigm.
Overall, we study practically relevant combinatorial problems, and we propose new algorithms to solve them. We demonstrate that these algorithms are suitable to handle massive datasets. However, our analysis is not problem-specific, and our results can be applied to other domains, if diminishing return properties hold. We hope that the flexibility of our framework inspires further research into scalability in AI.
Künstliche Intelligenz (KI) gewinnt in zahlreichen Branchen rasant an Bedeutung und wird zunehmend auch in Enterprise Resource Planning (ERP)-Systemen als Anwendungsbereich erschlossen. Die Idee, dass Maschinen die kognitiven Fähigkeiten des Menschen imitieren können, indem Wissen durch Lernen auf Basis von Beispielen in Daten, Informationen und Erfahrungen generiert wird, ist heute ein Schlüsselelement der digitalen Transformation. Jedoch charakterisiert der Einsatz von KI in ERP-System einen hohen Komplexitätsgrad, da die KI als Querschnittstechnologie zu verstehen ist, welche in unterschiedlichen Unternehmensbereichen zum Einsatz kommen kann. Auch die Anwendungsgrade können sich dabei erheblich voneinander unterscheiden. Um trotz dieser Komplexität den Einsatz der KI in ERP-Systemen erfassen und systembezogen vergleichen zu können, wurde im Rahmen dieser Studie ein Reifegradmodell entwickelt. Dieses bildet die Ausgangsbasis zur Ermittlung der KI-Reife in ERP-Systemen und grenzt dabei die folgenden vier KI- bzw. systembezogenen Ebenen voneinander ab: 1) Technische Möglichkeiten, 2) Datenreife, 3) Funktionsreife und 4) Erklärfähigkeit des Systems.
An ever-increasing number of prediction models is published every year in different medical specialties. Prognostic or diagnostic in nature, these models support medical decision making by utilizing one or more items of patient data to predict outcomes of interest, such as mortality or disease progression. While different computer tools exist that support clinical predictive modeling, I observed that the state of the art is lacking in the extent to which the needs of research clinicians are addressed. When it comes to model development, current support tools either 1) target specialist data engineers, requiring advanced coding skills, or 2) cater to a general-purpose audience, therefore not addressing the specific needs of clinical researchers. Furthermore, barriers to data access across institutional silos, cumbersome model reproducibility and extended experiment-to-result times significantly hampers validation of existing models. Similarly, without access to interpretable explanations, which allow a given model to be fully scrutinized, acceptance of machine learning approaches will remain limited. Adequate tool support, i.e., a software artifact more targeted at the needs of clinical modeling, can help mitigate the challenges identified with respect to model development, validation and interpretation. To this end, I conducted interviews with modeling practitioners in health care to better understand the modeling process itself and ascertain in what aspects adequate tool support could advance the state of the art. The functional and non-functional requirements identified served as the foundation for a software artifact that can be used for modeling outcome and risk prediction in health research. To establish the appropriateness of this approach, I implemented a use case study in the Nephrology domain for acute kidney injury, which was validated in two different hospitals. Furthermore, I conducted user evaluation to ascertain whether such an approach provides benefits compared to the state of the art and the extent to which clinical practitioners could benefit from it. Finally, when updating models for external validation, practitioners need to apply feature selection approaches to pinpoint the most relevant features, since electronic health records tend to contain several candidate predictors. Building upon interpretability methods, I developed an explanation-driven recursive feature elimination approach. This method was comprehensively evaluated against state-of-the art feature selection methods. Therefore, this thesis' main contributions are three-fold, namely, 1) designing and developing a software artifact tailored to the specific needs of the clinical modeling domain, 2) demonstrating its application in a concrete case in the Nephrology context and 3) development and evaluation of a new feature selection approach applicable in a validation context that builds upon interpretability methods. In conclusion, I argue that appropriate tooling, which relies on standardization and parametrization, can support rapid model prototyping and collaboration between clinicians and data scientists in clinical predictive modeling.