8 citations · 29 across the 15 of their papers we have counts for
4 papers · 1 filter
Whom do Explanations Serve? A Systematic Literature Survey of User Characteristics in Explainable Recommender Systems Evaluation
Kathrin Wardatzky, Oana Inel, Luca Rossetto +1
Adding explanations to recommender systems is said to have multiple benefits, such as increasing user trust or system transparency. Previous work from other application areas sugge…
Aligning Object Detector Bounding Boxes with Human Preference
Ombretta Strafforello, Osman S. Kayhan, Oana Inel +2
Previous work shows that humans tend to prefer large bounding boxes over small bounding boxes with the same IoU. However, we show here that commonly used object detectors predict l…
DWARF: Disease-weighted network for attention map refinement
Haozhe Luo, Aurélie Pahud de Mortanges, Oana Inel +2
The interpretability of deep learning is crucial for evaluating the reliability of medical imaging models and reducing the risks of inaccurate patient recommendations. This study a…
Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation
Jessica Quaye, Alicia Parrish, Oana Inel +12
With the rise of text-to-image (T2I) generative AI models reaching wide audiences, it is critical to evaluate model robustness against non-obvious attacks to mitigate the generatio…