708 citations · 1k across the 22 of their papers we have counts for
23 papers
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…
"Is a picture of a bird a bird": Policy recommendations for dealing with ambiguity in machine vision models
Alicia Parrish, Sarah Laszlo, Lora Aroyo
Many questions that we ask about the world do not have a single clear answer, yet typical human annotation set-ups in machine learning assume there must be a single ground truth la…
Intersectionality in Conversational AI Safety: How Bayesian Multilevel Models Help Understand Diverse Perceptions of Safety
Christopher M. Homan, Greg Serapio-Garcia, Lora Aroyo +5
Conversational AI systems exhibit a level of human-like behavior that promises to have profound impacts on many aspects of daily life -- how people access information, create conte…
DICES Dataset: Diversity in Conversational AI Evaluation for Safety
Lora Aroyo, Alex S. Taylor, Mark Diaz +5
Machine learning approaches often require training and evaluation datasets with a clear separation between positive and negative examples. This risks simplifying and even obscuring…
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…
Human-Centered Responsible Artificial Intelligence: Current & Future Trends
Mohammad Tahaei, Marios Constantinides, Daniele Quercia +13
In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence. While different research communities may use diffe…