---
res:
  bibo_abstract:
  - Human-AI collaboration is increasingly promoted to improve high-stakes decision-making,
    yet its benefits have not been fully realized. Application-grounded evaluations
    are needed to better evaluate methods for improving collaboration but often require
    domain experts, making studies costly and limiting their generalizability. Current
    evaluation methods are constrained by limited public datasets and reliance on
    proxy tasks. To address these challenges, we propose an application-grounded framework
    for large-scale, online evaluations of vision-based decision-making tasks. The
    framework introduces Blockies, a parametric approach for generating datasets of
    simulated diagnostic tasks, offering control over the traits and biases in the
    data used to train real-world models. These tasks are designed to be easy to learn
    but difficult to master, enabling participation by non-experts. The framework
    also incorporates storytelling and monetary incentives to manipulate perceived
    task stakes. An initial empirical study demonstrated that the high-stakes condition
    significantly reduced healthy distrust of AI, despite longer decision-making times.
    These findings underscore the importance of perceived stakes in fostering healthy
    distrust and demonstrate the framework's potential for scalable evaluation of
    high-stakes Human-AI collaboration. @eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: David S.
      foaf_name: Johnson, David S.
      foaf_surname: Johnson
  dct_date: 2025^xs_gYear
  dct_language: eng
  dct_title: Higher Stakes, Healthier Trust? An Application-Grounded Approach to Assessing
    Healthy Trust in High-Stakes Human-AI Collaboration@
...