Domenique Zipperling

Christiane Ernst, Luis Gutmann, Domenique Zipperling et al.

In high-stakes AI-supported decisions, considerations are not purely technical but involve moral judgments about fairness, responsibility, and harm. While prior research has focused mainly on functional or behavioral alignment, this paper argues that moral alignment may be a more fundamental dimension of human-AI decision-making. Moral alignment is defined as the perceived congruence between the values embedded in an AI system's decision logic and the moral intuitions of stakeholders. Building on Moral Foundations Theory, the paper adopts a multi-stakeholder perspective and highlights why moral (mis)alignment matters for the meaningful integration of AI in sensitive contexts.

Domenique Zipperling, Lukas Schmidt, Benedikt Hahn et al.

Current clinical decision support systems (CDSSs) typically base their predictions on correlation, not causation. In recent years, causal machine learning (ML) has emerged as a promising way to improve decision-making with CDSSs by offering interpretable, treatment-specific reasoning. However, existing research often emphasizes model development rather than designing clinician-facing interfaces. To address this gap, we investigated how CDSSs based on causal ML should be designed to effectively support collaborative clinical decision-making. Using a design science research methodology, we conducted a structured literature review and interviewed experienced physicians. From these, we derived eight empirically grounded design requirements, developed seven design principles, and proposed nine practical design features. Our results establish guidance for designing CDSSs that deliver causal insights, integrate seamlessly into clinical workflows, and support trust, usability, and human-AI collaboration. We also reveal tensions around automation, responsibility, and regulation, highlighting the need for an adaptive certification process for ML-based medical products.

Luca Deck, Jan-Laurin Müller, Conradin Braun et al.

The topic of fairness in AI, as debated in the FATE (Fairness, Accountability, Transparency, and Ethics in AI) communities, has sparked meaningful discussions in the past years. However, from a legal perspective, particularly from the perspective of European Union law, many open questions remain. Whereas algorithmic fairness aims to mitigate structural inequalities at design-level, European non-discrimination law is tailored to individual cases of discrimination after an AI model has been deployed. The AI Act might present a tremendous step towards bridging these two approaches by shifting non-discrimination responsibilities into the design stage of AI models. Based on an integrative reading of the AI Act, we comment on legal as well as technical enforcement problems and propose practical implications on bias detection and bias correction in order to specify and comply with specific technical requirements.

Marc Wegmann, Domenique Zipperling, Jonas Hillenbrand et al.

Data analysis plays an indispensable role for value creation in industry. Cluster analysis in this context is able to explore given datasets with little or no prior knowledge and to identify unknown patterns. As (big) data complexity increases in the dimensions volume, variety, and velocity, this becomes even more important. Many tools for cluster analysis have been developed from early on and the variety of different clustering algorithms is huge. As the selection of the right clustering procedure is crucial to the results of the data analysis, users are in need for support on their journey of extracting knowledge from raw data. Thus, the objective of this paper lies in the identification of a systematic selection logic for clustering algorithms and corresponding validation concepts. The goal is to enable potential users to choose an algorithm that fits best to their needs and the properties of their underlying data clustering problem. Moreover, users are supported in selecting the right validation concepts to make sense of the clustering results. Based on a comprehensive literature review, this paper provides assessment criteria for clustering method evaluation and validation concept selection. The criteria are applied to several common algorithms and the selection process of an algorithm is supported by the introduction of pseudocode-based routines that consider the underlying data structure.