Hudecek, Matthias

Profilbild
E-Mail-Adresse
Geburtsdatum
Projekt
Organisationseinheiten
Berufsbeschreibung
Nachname
Hudecek
Vorname
Matthias
Name
Matthias Hudecek

Filter

2023 - 20244
Filter zurücksetzen

Einstellungen

Autor:in: Cecil, Julia ×

Suchergebnisse

Gerade angezeigt 1 - 4 von 4
  • Vorschaubild
    Publikation
    Patient:innen und KI: Eine Frage der Perspektive bei der Bewertung von KI bei medizinischen Online-Diensten
    (Frankfurt University of Applied Sciences, 2024) Lermer, Eva; Gaube, Susanne; Cecil, Julia; Kleine, Anne-Kathrin; Kokje, Eesha; Frey, Dieter; Hudecek, Matthias; Klein, Barbara; Rägle, Susanne; Klüber, Susanne [in: Künstliche Intelligenz im Healthcare-Sektor]
    04A - Beitrag Sammelband
  • Vorschaubild nicht verfügbar
    Publikation
    Explainability does not mitigate the negative impact of incorrect AI advice in a personnel selection task
    (Nature, 2024) Cecil, Julia; Lermer, Eva; Hudecek, Matthias; Sauer, Jan; Gaube, Susanne [in: Scientific Reports]
    Despite the rise of decision support systems enabled by artificial intelligence (AI) in personnel selection, their impact on decision-making processes is largely unknown. Consequently, we conducted five experiments (N = 1403 students and Human Resource Management (HRM) employees) investigating how people interact with AI-generated advice in a personnel selection task. In all pre-registered experiments, we presented correct and incorrect advice. In Experiments 1a and 1b, we manipulated the source of the advice (human vs. AI). In Experiments 2a, 2b, and 2c, we further manipulated the type of explainability of AI advice (2a and 2b: heatmaps and 2c: charts). We hypothesized that accurate and explainable advice improves decision-making. The independent variables were regressed on task performance, perceived advice quality and confidence ratings. The results consistently showed that incorrect advice negatively impacted performance, as people failed to dismiss it (i.e., overreliance). Additionally, we found that the effects of source and explainability of advice on the dependent variables were limited. The lack of reduction in participants’ overreliance on inaccurate advice when the systems’ predictions were made more explainable highlights the complexity of human-AI interaction and the need for regulation and quality standards in HRM.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Fine for others but not for me: The role of perspective in patients’ perception of artificial intelligence in online medical platforms
    (Elsevier, 2024) Hudecek, Matthias; Lermer, Eva; Gaube, Susanne; Cecil, Julia; Heiss, Silke F.; Batz, Falk [in: Computers in Human Behavior: Artificial Humans]
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Surfing in the streets: How problematic smartphone use, fear of missing out, and antisocial personality traits are linked to driving behavior
    (Public Library of Science, 2023) Hudecek, Matthias; Lemster, Simon; Fischer, Peter; Cecil, Julia; Frey, Dieter; Gaube, Susanne; Lermer, Eva [in: PLOS ONE]
    Smartphone use while driving (SUWD) is a major cause of accidents and fatal crashes. This serious problem is still too little understood to be solved. Therefore, the current research aimed to contribute to a better understanding of SUWD by examining factors that have received little or no attention in this context: problematic smartphone use (PSU), fear of missing out (FOMO), and Dark Triad. In the first step, we conducted a systematic literature review to map the current state of research on these factors. In the second step, we conducted a cross-sectional study and collected data from 989 German car drivers. A clear majority (61%) admitted to using the smartphone while driving at least occasionally. Further, the results showed that FOMO is positively linked to PSU and that both are positively associated with SUWD. Additionally, we found that Dark Triad traits are relevant predictors of SUWD and other problematic driving behaviors––in particular, psychopathy is associated with committed traffic offenses. Thus, results indicate that PSU, FOMO, and Dark Triad are relevant factors to explain SUWD. We hope to contribute to a more comprehensive understanding of this dangerous phenomenon with these findings.
    01A - Beitrag in wissenschaftlicher Zeitschrift