Marc Schmitt

• PhD
• Senior IT Partner at Siemens

The digital age, driven by the AI revolution, brings significant opportunities but also conceals security threats, which we refer to as cyber shadows. These threats pose risks at individual, organizational, and societal levels. This paper examines the systemic impact of these cyber threats and proposes a comprehensive cybersecurity strategy that in...

The digital age, driven by the AI revolution, brings significant opportunities but also conceals security threats, which we refer to as cyber shadows. These threats pose risks at individual, organizational, and societal levels. This paper examines the systemic impact of these cyber threats and proposes a comprehensive cybersecurity strategy that in...

The digital age, driven by the AI revolution, brings significant opportunities but also conceals security threats, which we refer to as cyber shadows. These threats pose risks at individual, organizational, and societal levels. This paper examines the systemic impact of these cyber threats and proposes a comprehensive cybersecurity strategy that in...

This paper addresses the complexities inherent in AI product prototyping, focusing on the challenges posed by the probabilistic nature of AI behavior and the limited accessibility of prototyping tools to AI non-experts. A design science research (DSR) approach is presented, which culminates in a conceptual framework aimed at structuring the AI prot...

The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has profound implications for both the utility and security of our digital interactions. This paper investigates the transformative role of Generative AI in Social Engineering (SE) attacks. We conduct a systematic review of social engineering and AI capabilities and use a the...

This study evaluates the carbon footprint (CF) of Automated Machine Learning (AutoML) algorithms in AI development, examining three datasets to assess emissions across various run-times and countries. It is shown that the carbon intensity (CI) of these systems is significantly influenced by the energy sources powering the computational infrastructu...

This paper addresses the complexities inherent in AI product prototyping, focusing on the challenges posed by the probabilistic nature of AI behavior and the limited accessibility of prototyping tools to non-experts. A Design Science Research (DSR) approach is presented which culminates in a conceptual framework aimed at improving the AI prototypin...

This paper explores the integration of Artificial Intelligence (AI) in public governance, leveraging insights from an expert survey with 15 participants. The survey aimed to understand the perceptions and experiences of professionals at the intersection of AI and governance, including their views on the potential benefits, challenges, and ethical c...

The integration of Artificial Intelligence (AI) into corporate strategy has become a pivotal focus for organizations aiming to maintain a competitive advantage in the digital age. As AI reshapes business operations and drives innovation, the need for specialized leadership to effectively manage these changes becomes increasingly apparent. In this p...

The last decades have been characterized by unprecedented technological advances, many of them powered by modern technologies such as Artificial Intelligence (AI) and Machine Learning (ML). The world has become more digitally connected than ever, but we face major challenges. One of the most significant is cybercrime, which has emerged as a global...

The metaverse presents a new paradigm in digital interaction based on a vast number of emerging technologies and will change the way we interact with each other and the world. It will fundamentally transform the global economy and impact all its stakeholders in business, politics, and society. The objective of this paper is twofold: The first part...

The realization that AI-driven decision-making is indispensable in today's fast-paced and ultra-competitive marketplace has raised interest in industrial machine learning (ML) applications significantly. The current demand for analytics experts vastly exceeds the supply. One solution to this problem is to increase the user-friendliness of ML framew...

Our fast-paced digital economy shaped by global competition requires increased data-driven decision-making based on artificial intelligence (AI) and machine learning (ML). The benefits of deep learning (DL) are manifold, but it comes with limitations that have – so far – interfered with widespread industry adoption. This paper explains why DL – des...

Metaverse ecosystems—encompassing extended reality (XR) technologies like virtual and augmented reality—offer substantial opportunities for business innovation across sectors. This paper underscores the necessity of artificial intelligence (AI) and big data analytics (BDA) for businesses to capitalize on the digital economy's convergence of virtual...

The use of artificial intelligence (AI) and machine learning (ML) in decision support has become vital for financial services companies to remain competitive. Appraisal of AI/ML has largely focused on prediction accuracy. We argue the need to look beyond accuracy as the sole determinant for model choice. Explainable artificial intelligence (XAI) an...

The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has profound implications for both the utility and security of our digital interactions. This paper investigates the transformative role of Generative AI in Social Engineering (SE) attacks. We conduct a systematic review of social engineering and AI capabilities and use a the...

Todays world is digital, global, and interconnected and mobile devices are at the heart of modern communications in business, politics, and civil society. However, cyber threats are an omnipresent reality in our hyper-connected world. The world economic forum ranks cyber threats consistently among the global top security risks. Attacks on mobile de...

The realization that AI-driven decision-making is indispensable in todays fast-paced and ultra-competitive marketplace has raised interest in industrial machine learning (ML) applications significantly. The current demand for analytics experts vastly exceeds the supply. One solution to this problem is to increase the user-friendliness of ML framewo...

Artificial intelligence (AI) and machine learning (ML) have become vital to remain competitive for financial services companies around the globe. The two models currently competing for the pole position in credit risk management are deep learning (DL) and gradient boosting machines (GBM). This paper benchmarked those two algorithms in the context o...

Our fast-paced digital economy shaped by global competition requires increased data-driven decision-making based on artificial intelligence (AI) and machine learning (ML). The benefits of deep learning (DL) are manifold, but it comes with limitations that have - so far - interfered with widespread industry adoption. This paper explains why DL - des...

This Ph.D. thesis explores the strength and applicability of machine learning-based classifiers within the context of business analytics for data-driven decision making. The focus is on supervised binary classification on structured datasets, which are vastly present in relational databases across all enterprises. Advanced analytics has become indi...