3 citations · 5 across the 6 of their papers we have counts for
6 papers
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…
Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection
Oana Inel, Tim Draws, Lora Aroyo
The rapid entry of machine learning approaches in our daily activities and high-stakes domains demands transparency and scrutiny of their fairness and reliability. To help gauge ma…
Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models
Alicia Parrish, Hannah Rose Kirk, Jessica Quaye +10
The generative AI revolution in recent years has been spurred by an expansion in compute power and data quantity, which together enable extensive pre-training of powerful text-to-i…
Humans disagree with the IoU for measuring object detector localization error
Ombretta Strafforello, Vanathi Rajasekart, Osman S. Kayhan +2
The localization quality of automatic object detectors is typically evaluated by the Intersection over Union (IoU) score. In this work, we show that humans have a different view on…