1 citations · 2 across the 5 of their papers we have counts for
8 papers
Markedness in Visual Semantic AI
Robert Wolfe, Aylin Caliskan
We evaluate the state-of-the-art multimodal "visual semantic" model CLIP ("Contrastive Language Image Pretraining") for biases related to the marking of age, gender, and race or et…
Evidence for Hypodescent in Visual Semantic AI
Robert Wolfe, Mahzarin R. Banaji, Aylin Caliskan
We examine the state-of-the-art multimodal "visual semantic" model CLIP ("Contrastive Language Image Pretraining") for the rule of hypodescent, or one-drop rule, whereby multiracia…
Contrastive Visual Semantic Pretraining Magnifies the Semantics of Natural Language Representations
Robert Wolfe, Aylin Caliskan
We examine the effects of contrastive visual semantic pretraining by comparing the geometry and semantic properties of contextualized English language representations formed by GPT…
VAST: The Valence-Assessing Semantics Test for Contextualizing Language Models
Robert Wolfe, Aylin Caliskan
VAST, the Valence-Assessing Semantics Test, is a novel intrinsic evaluation task for contextualized word embeddings (CWEs). VAST uses valence, the association of a word with pleasa…
Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models
Robert Wolfe, Aylin Caliskan
We use a dataset of U.S. first names with labels based on predominant gender and racial group to examine the effect of training corpus frequency on tokenization, contextualization,…
Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases
Ryan Steed, Aylin Caliskan
Recent advances in machine learning leverage massive datasets of unlabeled images from the web to learn general-purpose image representations for tasks from image classification to…