Michaela Benk

Michaela Benk, Tim Miller

The HCI community commonly evaluates decision support systems based on whether they improve task performance or promote appropriate user reliance. In this work, we look beyond decision outcomes to examine the process through which users develop decision-making strategies. Through a web-based experiment (N = 290) comparing recommendation-driven and hypothesis-driven interaction designs, and using Signal Detection Theory as a theoretical framework, we show that even when performance remains identical, recommendation-driven designs lower participants' thresholds for sufficient evidence and introduce a "hidden bias" in their judgments, resulting in a shifted distribution of errors. Furthermore, we find that experts are just as susceptible to these systemic shifts as novices. We conclude by advocating for a shift in focus: prioritizing decision processes and the preservation of stable evidence standards over performance and reliance alone.

Aman Kumar, Maximilian Tornow, Michaela Benk et al.

Personalizing item ranking creation is a challenging task, especially when users lack knowledge of data attributes or the ability to express and formalize their attribute preferences. Item-based ranking creation is an approach allowing users to directly externalize preferences through known-item judgments rather than attribute-based scoring. However, a core challenge of item-based ranking is identifying and selecting representative candidate items for externalizing preferences. Existing approaches rely on singular item-selection methods, limiting flexibility and user control. To address this challenge, we present Ranking Companion, a visual analytics approach for item-based ranking that combines model-driven active learning with human-driven item-selection methods. By drawing from six complementary item-selection methods, users can externalize listwise preferences based on selected candidate items, while an iterative machine learning process with a ranking model calculates ranking results, presented to users alongside explanations for interpretation. We evaluated Ranking Companion in a formative user study with 10 participants, in which participants used each item-selection method across three iterations, revealing tradeoffs in perceived ranking quality across accuracy, diversity, novelty, transparency, control, and satisfaction. Ranking Companion contributes a unified interactive item selection space and provides preliminary empirical guidance toward the hybrid use of multiple complementary item-selection methods in personalized item-based ranking creation.

Nicolas Scharowski, Michaela Benk, Swen J. Kühne et al.

Auditing plays a pivotal role in the development of trustworthy AI. However, current research primarily focuses on creating auditable AI documentation, which is intended for regulators and experts rather than end-users affected by AI decisions. How to communicate to members of the public that an AI has been audited and considered trustworthy remains an open challenge. This study empirically investigated certification labels as a promising solution. Through interviews (N = 12) and a census-representative survey (N = 302), we investigated end-users' attitudes toward certification labels and their effectiveness in communicating trustworthiness in low- and high-stakes AI scenarios. Based on the survey results, we demonstrate that labels can significantly increase end-users' trust and willingness to use AI in both low- and high-stakes scenarios. However, end-users' preferences for certification labels and their effect on trust and willingness to use AI were more pronounced in high-stake scenarios. Qualitative content analysis of the interviews revealed opportunities and limitations of certification labels, as well as facilitators and inhibitors for the effective use of labels in the context of AI. For example, while certification labels can mitigate data-related concerns expressed by end-users (e.g., privacy and data protection), other concerns (e.g., model performance) are more challenging to address. Our study provides valuable insights and recommendations for designing and implementing certification labels as a promising constituent within the trustworthy AI ecosystem.