Niklas Kühl

University of Bayreuth · Professorship of Information Systems and human-centric Artificial Intelligence

As the shortage of skilled workers continues to be a pressing issue, exacerbated by demographic change, it is becoming a critical challenge for organizations to preserve the knowledge of retiring experts and pass it on to novices. While this knowledge transfer has traditionally occurred through personal interaction, it lacks scalability and require...

We explore the implementation of deep learning techniques for precise building damage assessment in the context of natural hazards, utilizing remote sensing data. The xBD dataset, comprising diverse disaster events from across the globe, serves as the primary focus, facilitating the evaluation of deep learning models. We tackle the challenges of ge...

Explainability techniques are rapidly being developed to improve human-AI decision-making across various cooperative work settings. Consequently, previous research has evaluated how decision-makers collaborate with imperfect AI by investigating appropriate reliance and task performance with the aim of designing more human-centered computer-supporte...

Recent research suggests that combining AI models with a human expert can exceed the performance of either alone. The combination of their capabilities is often realized by learning to defer algorithms that enable the AI to learn to decide whether to make a prediction for a particular instance or defer it to the human expert. However, to accurately...

In AI-assisted decision-making, a central promise of putting a human in the loop is that they should be able to complement the AI system by adhering to its correct and overriding its mistaken recommendations. In practice, however, we often see that humans tend to over- or under-rely on AI recommendations, meaning that they either adhere to wrong or...

Successful analytics solutions that provide valuable insights often hinge on the connection of various data sources. While it is often feasible to generate larger data pools within organizations, the application of analytics within (inter-organizational) business networks is still severely constrained. As data is distributed across several legal un...

Cost-effective and responsible use of cloud computing resources (CCR) is on the business agenda of many companies. Despite this strategic goal, two geopolitical strategy decisions mainly influence the continuous existence of overcapacity: Europe's General Data Protection Regulation and the US's Cloud Act. Given the circumstances, a typical data cen...

As the shortage of skilled workers continues to be a pressing issue, exacerbated by demographic change, it is becoming a critical challenge for organizations to preserve the knowledge of retiring experts and to pass it on to novices. While this knowledge transfer has traditionally taken place through personal interaction, it lacks scalability and r...

With the increased adoption of artificial intelligence (AI) in industry and society, effective human-AI interaction systems are becoming increasingly important. A central challenge in the interaction of humans with AI is the estimation of difficulty for human and AI agents for single task instances. These estimations are crucial to evaluate each ag...

With the increased adoption of artificial intelligence (AI) in industry and society, effective human-AI interaction systems are becoming increasingly important. A central challenge in the interaction of humans with AI is the estimation of difficulty for human and AI agents for single task instances.These estimations are crucial to evaluate each age...

In AI-assisted decision-making, a central promise of putting a human in the loop is that they should be able to complement the AI system by adhering to its correct and overriding its mistaken recommendations. In practice, however, we often see that humans tend to over- or under-rely on AI recommendations, meaning that they either adhere to wrong or...

Recent research suggests that combining AI models with a human expert can exceed the performance of either alone. The combination of their capabilities is often realized by learning to defer algorithms that enable the AI to learn to decide whether to make a prediction for a particular instance or defer it to the human expert. However, to accurately...

Successful analytics solutions that provide valuable insights often hinge on the connection of various data sources. While it is often feasible to generate larger data pools within organizations, the application of analytics within (inter-organizational) business networks is still severely constrained. As data is distributed across several legal un...

The usage and impact of deep learning for cleaner production and sustainability purposes remain little explored. This work shows how deep learning can be harnessed to increase sustainability in production and product usage. Specifically, we utilize deep learning-based computer vision to determine the wear states of products. The resulting insights...

Detecting rare events is essential in various fields, e.g., in cyber security or maintenance. Often, human experts are supported by anomaly detection systems as continuously monitoring the data is an error-prone and tedious task. However, among the anomalies detected may be events that are rare, e.g., a planned shutdown of a machine, but are not th...

AI advice is becoming increasingly popular, e.g., in investment and medical treatment decisions. As this advice is typically imperfect, decision-makers have to exert discretion as to whether actually follow that advice: they have to "appropriately" rely on correct and turn down incorrect advice. However, current research on appropriate reliance sti...

With the increasing importance of data and artificial intelligence, organizations strive to become more data-driven. However, current data architectures are not necessarily designed to keep up with the scale and scope of data and analytics use cases. In fact, existing architectures often fail to deliver the promised value associated with them. Data...

In this work, we empirically examine human-AI decision-making in the presence of explanations based on predicted outcomes. This type of explanation provides a human decision-maker with expected consequences for each decision alternative at inference time—where the predicted outcomes are typically measured in a problem-specific unit (e.g., profit in...

Climate change results in an increased probability of extreme weather events that put societies and businesses at risk on a global scale. Therefore, near real-time mapping of natural hazards is an emerging priority for the support of natural disaster relief, risk management, and informing governmental policy decisions. Recent methods to achieve nea...

The aftermath of air raids can still be seen for decades after the devastating events. Unexploded ordnance (UXO) is an immense danger to human life and the environment. Through the assessment of wartime images, experts can infer the occurrence of a dud. The current manual analysis process is expensive and time-consuming, thus automated detection of...

The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. However, it is highly challenging to automate and operationalize ML products and thus many ML endeavors fail to deliver on their expectations. The paradigm of Machine Learning Operations (MLOps) addresses this issue. MLO...

Within the last decade, the application of “artificial intelligence” and “machine learning” has become popular across multiple disciplines, especially in information systems. The two terms are still used inconsistently in academia and industry—sometimes as synonyms, sometimes with different meanings. With this work, we try to clarify the relationsh...

Modern vehicles typically are equipped with assistance systems to support drivers in staying vigilant. To assess the driver state, such systems usually split characteristic vehicle signals into smaller segments which are subsequently fed into algorithms to identify irregularities in driver behavior. In this paper, we compare four different approach...

Explanations have been framed as an essential feature for better and fairer human-AI decision-making. In the context of fairness, this has not been appropriately studied, as prior works have mostly evaluated explanations based on their effects on people's perceptions. We argue, however, that for explanations to promote fairer decisions, they must e...

The aftermath of air raids can still be seen for decades after the devastating events. Unexploded ordnance (UXO) is an immense danger to human life and the environment. Through the assessment of wartime images, experts can infer the occurrence of a dud. The current manual analysis process is expensive and time-consuming, thus automated detection of...

Customers in the manufacturing sector, especially in the automotive industry, have a high demand for individualized products at price levels comparable to traditional mass-production. The contrary objectives of providing a variety of products and operating at minimum costs have introduced a high degree of production planning and control mechanisms...

The capabilities of supervised machine learning (SML), especially compared to human abilities, are being discussed in scientific research and in the usage of SML. This study provides an answer to how learning performance differs between humans and machines when there is limited training data. We have designed an experiment in which 44 humans and th...

In this work, we empirically examine human-AI decision-making in the presence of explanations based on predicted outcomes. This type of explanation provides a human decision-maker with expected consequences for each decision alternative at inference time - where the predicted outcomes are typically measured in a problem-specific unit (e.g., profit...

Research in artificial intelligence (AI)-assisted decision-making is experiencing tremendous growth with a constantly rising number of studies evaluating the effect of AI with and without techniques from the field of explainable AI (XAI) on human decision-making performance. However, as tasks and experimental setups vary due to different objectives...

Research in artificial intelligence (AI)-assisted decision-making is experiencing tremendous growth with a constantly rising number of studies evaluating the effect of AI with and without techniques from the field of explainable AI (XAI) on human decision-making performance. However, as tasks and experimental setups vary due to different objectives...

Across a multitude of work environments, expert knowledge is imperative for humans to conduct tasks with high performance and ensure business success. These humans possess task-specific expert knowledge (TSEK) and hence, represent subject matter experts (SMEs). However, not only demographic changes but also personnel downsizing strategies lead and...

Recent developments in Artificial Intelligence (AI) have fueled the emergence of human-AI collaboration, a setting where AI is a coequal partner. Especially in clinical decision-making, it has the potential to improve treatment quality by assisting overworked medical professionals. Even though research has started to investigate the utilization of...

Contemporary information systems make widespread use of artificial intelligence (AI). While AI offers various benefits, it can also be subject to systematic errors, whereby people from certain groups (defined by gender, age, or other sensitive attributes) experience disparate outcomes. In many AI applications, disparate outcomes confront businesses...

The field of artificial intelligence (AI) is advancing quickly, and systems can increasingly perform a multitude of tasks that previously required human intelligence. Information systems can facilitate collaboration between humans and AI systems such that their individual capabilities complement each other. However, there is a lack of consolidated...

Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not allow for understanding how a given decision was arrived at. In this work, we conduct a human subject study to assess people's perceptions of informational fairness (i.e...

The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. However, it is highly challenging to automate and operationalize ML products and thus many ML endeavors fail to deliver on their expectations. The paradigm of Machine Learning Operations (MLOps) addresses this issue. MLO...

Over the last years, the rising capabilities of artificial intelligence (AI) have improved human decision-making in many application areas. Teaming between AI and humans may even lead to complementary team performance (CTP), i.e., a level of performance beyond the ones that can be reached by AI or humans individually. Many researchers have proposed...

It is known that recommendations of AI-based systems can be incorrect or unfair. Hence, it is often proposed that a human be the final decision-maker. Prior work has argued that explanations are an essential pathway to help human decision-makers enhance decision quality and mitigate bias, i.e., facilitate human-AI complementarity. For these benefit...

Artificial intelligence (AI) is gaining momentum, and its importance for the future of work in many areas, such as medicine and banking, is continuously rising. However, insights on the effective collaboration of humans and AI are still rare. Typically, AI supports humans in decision-making by addressing human limitations. However, it may also evok...

Recent developments in Artificial Intelligence (AI) have fueled the emergence of human-AI collaboration, a setting where AI is a coequal partner. Especially in clinical decision-making, it has the potential to improve treatment quality by assisting overworked medical professionals. Even though research has started to investigate the utilization of...

Many important decisions in daily life are made with the help of advisors, e.g., decisions about medical treatments or financial investments. Whereas in the past, advice has often been received from human experts, friends, or family, advisors based on artificial intelligence (AI) have become more and more present nowadays. Typically, the advice gen...

With the ever-increasing societal dependence on electricity, one of the critical tasks in power supply is maintaining the power line infrastructure. In the process of making informed, cost-effective, and timely decisions, maintenance engineers must rely on human-created, heterogeneous, structured, and also largely unstructured information. The matu...

The rapid development of 3D sensors and object detection methods based on 3D point clouds has led to increasing demand for labeling tools that provide suitable training data. However, existing labeling tools mostly focus on a single use case and generate bounding boxes only indirectly from a selection of points, which often impairs the label qualit...

The importance of deployed machine learning solutions has increased significantly in the past years due to the availability of data sources, computing capabilities and convenient tooling. However, technical challenges such as limited resources and computing power arise in many applications. We consider a scenario where a machine learning model is d...

Deep learning methods have proven to outperform traditional computer vision methods in various areas of image processing. However, the application of deep learning in industrial surface defect detection systems is challenging due to the insufficient amount of training data, the expensive data generation process, the small size, and the rare occurre...

Convolutional Neural Networks (CNNs) have proven to be state-of-the-art models for supervised computer vision tasks, such as image classification. However, large labeled datasets are generally needed for the training and validation of such models. In many domains, unlabeled data is available but labeling is expensive, for instance, when specific ex...

Computer-generated imagery of car models has become an indispensable part of car manufacturers' advertisement concepts. They are for instance used in car configurators to offer customers the possibility to configure their car online according to their personal preferences. However, human-led quality assurance faces the challenge to keep up with hig...

While recent advances in AI-based automated decision-making have shown many benefits for businesses and society, they also come at a cost. It has for long been known that a high level of automation of decisions can lead to various drawbacks, such as automation bias and deskilling. In particular, the deskilling of knowledge workers is a major issue,...

Managing large numbers of incoming bug reports and finding the most critical issues in hardware development is time consuming, but crucial in order to reduce development costs. In this paper, we present an approach to predict the time to fix, the risk and the complexity of debugging and resolution of a bug report using different supervised machine...

Deep learning methods have proven to outperform traditional computer vision methods in various areas of image processing. However, the application of deep learning in industrial surface defect detection systems is challenging due to the insufficient amount of training data, the expensive data generation process, the small size, and the rare occurre...

Fake news has become omnipresent in digitalized areas such as social media platforms. While being disseminated online, it also poses a threat to individuals and societies offline, for example, in the context of democratic elections. Research and practice have investigated the detection of fake news with behavioral science or method-related perspect...

Automated decision systems (ADS) have become ubiquitous in many high-stakes domains. Those systems typically involve sophisticated yet opaque artificial intelligence (AI) techniques that seldom allow for full comprehension of their inner workings, particularly for affected individuals. As a result, ADS are prone to deficient oversight and calibrati...

With the continuing decrease of sensor technology prices as well as the increase of communication and analytical capabilities of modern internet of things devices, the continuously generated amount of data is constantly growing. Various use cases show the untapped potential of this data for new business models. However, conventional industrial IT n...

With the continuing decrease of sensor technology prices as well as the increase of communication and analytical capabilities of modern internet of things devices, the continuously generated amount of data is constantly growing. Various use cases show the untapped potential of this data for new business models. However, conventional industrial IT n...

It is often argued that one goal of explaining automated decision systems (ADS) is to facilitate positive perceptions (e.g., fairness or trustworthiness) of users towards such systems. This viewpoint, however, makes the implicit assumption that a given ADS is fair and trustworthy, to begin with. If the ADS issues unfair outcomes, then one might exp...

Hybrid Intelligence is an emerging concept that emphasizes the complementary nature of human intelligence and artificial intelligence (AI). One key requirement for collaboration between humans and AI is the interpretability of the decisions provided by the AI to enable humans to assess whether to comply with the presented decisions. Due to the blac...

Deployed machine learning models are confronted with the problem of changing data over time, a phenomenon also called concept drift. While existing approaches of concept drift detection already show convincing results, they require true labels as a prerequisite for successful drift detection. Especially in many real-world application scenarios-like...

Computer vision (CV) techniques try to mimic human capabilities of visual perception to support labor-intensive and time-consuming tasks like the recognition and localization of critical objects. Nowadays, CV increasingly relies on artificial intelligence (AI) to automatically extract useful information from images that can be utilized for decision...

The increasing volume of external shocks, such as the financial crisis or the COVID-19 pandemic, emphasizes the need for businesses to increase their resilience, i.e., a system’s ability to reduce the impact or recover from such shocks quickly. In the past, information systems were often viewed as a source of vulnerability and complexity. In contra...