arXiv subjects

Nicolas Scharowski

Publications and source records attributed to Nicolas Scharowski.

5 recordsLinked to original sources

arXiv2025

Biased Minds Meet Biased AI: How Class Imbalance Shapes Appropriate Reliance and Interacts with Human Base Rate Neglect

Humans increasingly interact with artificial intelligence (AI) in decision-making. However, both AI and humans are prone to biases. While AI and human biases have been studied extensively in isolation, this paper examines their complex interaction. Specifically, we examined how class imbalance as an AI bias affects people's ability to appropriately rely on an AI-based decision-support system, and how it interacts with base rate neglect as a human bias. In a within-subject online study (N= 46), participants classified three diseases using an AI-based decision-support system trained on either a balanced or unbalanced dataset. We found that class imbalance disrupted participants' calibration of AI reliance. Moreover, we observed mutually reinforcing effects between class imbalance and base rate neglect, offering evidence of a compound human-AI bias. Based on these findings, we advocate for an interactionist perspective and further research into the mutually reinforcing effects of biases in human-AI interaction.

cs.HC↗

arXiv2024

To Trust or Distrust Trust Measures: Validating Questionnaires for Trust in AI

Despite the importance of trust in human-AI interactions, researchers must adopt questionnaires from other disciplines that lack validation in the AI context. Motivated by the need for reliable and valid measures, we investigated the psychometric quality of two trust questionnaires, the Trust between People and Automation scale (TPA) by Jian et al. (2000) and the Trust Scale for the AI Context (TAI) by Hoffman et al. (2023). In a pre-registered online experiment (N = 1485), participants observed interactions with trustworthy and untrustworthy AI (autonomous vehicle and chatbot). Results support the psychometric quality of the TAI while revealing opportunities to improve the TPA, which we outline in our recommendations for using the two questionnaires. Furthermore, our findings provide additional empirical evidence of trust and distrust as two distinct constructs that may coexist independently. Building on our findings, we highlight the opportunities and added value of measuring both trust and distrust in human-AI research and advocate for further work on both constructs.

cs.HC↗

arXiv2023

Certification Labels for Trustworthy AI: Insights From an Empirical Mixed-Method Study

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.

cs.CY↗

arXiv2023

Distrust in (X)AI -- Measurement Artifact or Distinct Construct?

Trust is a key motivation in developing explainable artificial intelligence (XAI). However, researchers attempting to measure trust in AI face numerous challenges, such as different trust conceptualizations, simplified experimental tasks that may not induce uncertainty as a prerequisite for trust, and the lack of validated trust questionnaires in the context of AI. While acknowledging these issues, we have identified a further challenge that currently seems underappreciated - the potential distinction between trust as one construct and \emph{distrust} as a second construct independent of trust. While there has been long-standing academic discourse for this distinction and arguments for both the one-dimensional and two-dimensional conceptualization of trust, distrust seems relatively understudied in XAI. In this position paper, we not only highlight the theoretical arguments for distrust as a distinct construct from trust but also contextualize psychometric evidence that likewise favors a distinction between trust and distrust. It remains to be investigated whether the available psychometric evidence is sufficient for the existence of distrust or whether distrust is merely a measurement artifact. Nevertheless, the XAI community should remain receptive to considering trust and distrust for a more comprehensive understanding of these two relevant constructs in XAI.

cs.HC↗

arXiv2022

Trust and Reliance in XAI -- Distinguishing Between Attitudinal and Behavioral Measures

Trust is often cited as an essential criterion for the effective use and real-world deployment of AI. Researchers argue that AI should be more transparent to increase trust, making transparency one of the main goals of XAI. Nevertheless, empirical research on this topic is inconclusive regarding the effect of transparency on trust. An explanation for this ambiguity could be that trust is operationalized differently within XAI. In this position paper, we advocate for a clear distinction between behavioral (objective) measures of reliance and attitudinal (subjective) measures of trust. However, researchers sometimes appear to use behavioral measures when intending to capture trust, although attitudinal measures would be more appropriate. Based on past research, we emphasize that there are sound theoretical reasons to keep trust and reliance separate. Properly distinguishing these two concepts provides a more comprehensive understanding of how transparency affects trust and reliance, benefiting future XAI research.

cs.HC↗