@article{Kaufman25vis,
  author = {Zhanna Kaufman and Madeline Endres and Cindy Xiong Bearfield and Yuriy Brun},
  title = {Your Model Is Unfair, Are You Even Aware? 
           Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models},
  journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)},
  venue = {TVCG},
  year = {2026},
  volume={32},
  number={1},
  pages={637--647},
  accept = {$\frac{131}{537} \approx 24\%$},

  doi = {10.1109/TVCG.2025.3634245},
  note = {Graphics Replicability Stamp Initiative: 
  Replicability Stamp. 
  Accepted as part of IEEE Visualization \& Visual Analytics (VIS) Conference, Vienna, Austria, November 2--7, 2025.
  DOI:
   10.1109/TVCG.2025.3634245},

  abstract = {Systems relying on ML have become ubiquitous, but so has biased behavior
  within them. Research shows that bias significantly affects stakeholders'
  trust in systems and how they use them. Further, stakeholders of different
  backgrounds view and trust the same systems differently. Thus, how ML models'
  behavior is explained plays a key role in comprehension and trust. We survey
  explainability visualizations, creating a taxonomy of design characteristics.
  We conduct user studies to evaluate five state-of-the-art visualization tools
  (LIME, SHAP, CP, Anchors, and ELI5) for model explainability, measuring how
  taxonomy characteristics affect comprehension, bias perception, and trust for
  non-expert ML users. Surprisingly, we find an inverse relationship between
  comprehension and trust: the better users understand the models, the less
  they trust them. We investigate the cause and find that this relationship is
  strongly mediated by bias perception: more comprehensible visualizations
  increase people's perception of bias, and increased bias perception reduces
  trust. We confirm this relationship is causal: Manipulating explainability
  visualizations to control comprehension, bias perception, and trust, we show
  that visualization design can significantly ($p<0.001$) increase
  comprehension, increase perceived bias, and reduce trust. Conversely,
  reducing perceived model bias, either by improving model fairness or by
  adjusting visualization design, significantly increases trust even when
  comprehension remains high. Our work advances understanding of how
  comprehension affects trust and systematically investigates visualization's
  role in facilitating responsible ML applications.
},
  
  
  fundedBy = {NSF CCF-2210243},
}