8 citations · 15 across the 14 of their papers we have counts for
6 papers · 1 filter
Calibrating LLM Explanations to Human Decisions with Reinforcement Learning
Dong Shu, Ziyang Guo, Negar Kamali +2
Explanations of LLM outputs are considered an important way to make model behavior legible, helping human users make good decisions on downstream tasks. However, default training a…
Generative AI Literacy Training Improves Intelligence Analysts' Discrimination of Real and AI-Generated Images
Negar Kamali, Candice Rockell Gerstner, Jessica Hullman +1
Across social and online platforms, people are increasingly exposed to AI-generated images. As a consequence, the task of distinguishing AI-generated from authentic images is becom…
Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization
Sheng Long, Angelos Chatzimparmpas, Emma Alexander +2
Judging the similarity of visualizations is crucial to various applications, such as visualization-based search and visualization recommendation systems. Recent studies show deep-f…
Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images
Negar Kamali, Karyn Nakamura, Aakriti Kumar +3
Diffusion model-generated images can appear indistinguishable from authentic photographs, but these images often contain artifacts and implausibilities that reveal their AI-generat…
How to Distinguish AI-Generated Images from Authentic Photographs
Negar Kamali, Karyn Nakamura, Angelos Chatzimparmpas +2
The high level of photorealism in state-of-the-art diffusion models like Midjourney, Stable Diffusion, and Firefly makes it difficult for untrained humans to distinguish between re…
Underspecified Human Decision Experiments Considered Harmful
Jessica Hullman, Alex Kale, Jason Hartline
Decision-making with information displays is a key focus of research in areas like human-AI collaboration and data visualization. However, what constitutes a decision problem, and…