Marc Schmitt, Pantelis Koutroumpis

Abstract: 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 integrates AI-driven solutions, such as Intrusion Detection Systems (IDS), with targeted policy interventions. By combining technological and regulatory measures, we create a multilevel defense capable of addressing both direct threats and indirect negative externalities. We emphasize that the synergy between AI-driven solutions and policy interventions is essential for neutralizing cyber threats and mitigating their negative impact on the digital economy. Finally, we underscore the need for continuous adaptation of these strategies, especially in response to the rapid advancement of autonomous AI-driven attacks, to ensure the creation of secure and resilient digital ecosystems.

Keywords: Artificial Intelligence, Cybersecurity, Policy, Threat Detection, Digital Trust

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Marc Schmitt, Ivan Flechais

Abstract: 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 theory of social engineering to identify three pillars where Generative AI amplifies the impact of SE attacks: Realistic Content Creation, Advanced Targeting and Personalization, and Automated Attack Infrastructure. We integrate these elements into a conceptual model designed to investigate the complex nature of AI-driven SE attacks—the Generative AI Social Engineering Framework. We further explore human implications and potential countermeasures to mitigate these risks. Our study aims to foster a deeper understanding of the risks, human implications, and countermeasures associated with this emerging paradigm, thereby contributing to a more secure and trustworthy human-computer interaction.

Keywords: Artificial intelligence, Machine learning, Social engineering, Phishing, ChatGPT, Large language models

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Mario Truss, Marc Schmitt

Abstract: This paper addresses 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 for structuring the AI prototyping process with no-code AutoML technologies for textual and tabular ML use cases. Through a comprehensive literature review, key challenges were identified, and no-code AutoML was positioned as a solution. The framework describes the incorporation of non-expert input and evaluation during prototyping, leveraging the potential of no-code AutoML to enhance accessibility and interpretability. A hybrid approach combining naturalistic (case study) and artificial evaluation methods (criteria-based analysis) validated the utility of our approach, highlighting its efficacy in supporting AI non-experts and streamlining decision-making and its limitations. The implications for academia and industry focus on the strategic integration of no-code AutoML to enhance AI product development processes, mitigate risks, and foster innovation.

Keywords: AutoML, Prototyping, Human-Centered Artificial Intelligence, Digital Innovation, Product Management, Human-AI Interaction, AutoML, Machine Learning

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Marc Schmitt

Abstract: 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 infrastructure. A correlation between run-time and model accuracy is observed, showing diminishing returns in accuracy with increased run-time and its environmental cost. The findings highlight the crucial role of geographic location and regional energy mix in determining the carbon footprint of AI operations. In areas with low-carbon or renewable energy sources, AI systems exhibit a reduced carbon footprint, underscoring the importance of infrastructural and environmental context in AI’s ecological impact. This study calls for adopting energy-efficient locations and optimizing ML algorithms to achieve a balance between model accuracy and environmental costs.

Keywords: Carbon Footprint, Sustainability, Machine Learning, AutoML

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Marc Schmitt

Abstract: 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 threat to governments, businesses, and civil societies. The pervasiveness of digital technologies combined with a constantly shifting technological foundation has created a complex and powerful playground for cybercriminals, which triggered a surge in demand for intelligent threat detection systems based on machine and deep learning. This paper investigates AI-based cyber threat detection to protect our modern digital ecosystems. The primary focus is on evaluating ML-based classifiers and ensembles for anomaly-based malware detection and network intrusion detection and how to integrate those models in the context of network security, mobile security, and IoT security. The discussion highlights the challenges when deploying and integrating AI-enabled cybersecurity solutions into existing enterprise systems and IT infrastructures, including options to overcome those challenges. Finally, the paper provides future research directions to further increase the security and resilience of our modern digital industries, infrastructures, and ecosystems.

Keywords: Cybersecurity, Machine learning, Digital ecosystems, Internet of things, Cyber-physical systems, Industry 5.0

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Marc Schmitt

Abstract: 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 frameworks to make them more accessible for the non-expert. Automated machine learning (AutoML) is an attempt to solve the problem of expertise by providing fully automated off-the-shelf solutions for model choice and hyperparameter tuning. This paper analyzed the potential of AutoML for applications within business analytics, which could help to increase the adoption rate of ML across all industries. The H2O AutoML framework was benchmarked against a manually tuned stacked ML model on three real-world datasets. The manually tuned ML model could reach a performance advantage in all three case studies used in the experiment. Nevertheless, the H2O AutoML package proved to be quite potent. It is fast, easy to use, and delivers reliable results, which come close to a professionally tuned ML model. The H2O AutoML framework in its current capacity is a valuable tool to support fast prototyping with the potential to shorten development and deployment cycles. It can also bridge the existing gap between supply and demand for ML experts and is a big step towards automated decisions in business analytics. Finally, AutoML has the potential to foster human empowerment in a world that is rapidly becoming more automated and digital.

Keywords: Artificial intelligence, Machine learning, AutoML, Business analytics, Data-driven decision making, Digital transformation, Human empowerment

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Marc Schmitt

Abstract: 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 – despite its popularity – has difficulties speeding up its adoption within business analytics. It is shown that the adoption of deep learning is not only affected by computational complexity, lacking big data architecture, lack of transparency (black-box), skill shortage, and leadership commitment, but also by the fact that DL does not outperform traditional ML models in the case of structured datasets with fixed-length feature vectors. Deep learning should be regarded as a powerful addition to the existing body of ML models instead of a “one size fits all” solution. The results strongly suggest that gradient boosting can be seen as the go-to model for predictions on structured datasets within business analytics. In addition to the empirical study based on three industry use cases, the paper offers a comprehensive discussion of those results, practical implications, and a roadmap for future research.

Keywords: Deep Learning, Machine learning, Business analytics, Artificial intelligence, Data-driven decision making, Digital transformation, Digital strategy

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