78 citations · 135 across the 8 of their papers we have counts for
8 papers
Evaluating Biased Attitude Associations of Language Models in an Intersectional Context
Shiva Omrani Sabbaghi, Robert Wolfe, Aylin Caliskan
Language models are trained on large-scale corpora that embed implicit biases documented in psychology. Valence associations (pleasantness/unpleasantness) of social groups determin…
Contrastive Language-Vision AI Models Pretrained on Web-Scraped Multimodal Data Exhibit Sexual Objectification Bias
Robert Wolfe, Yiwei Yang, Bill Howe +1
Nine language-vision AI models trained on web scrapes with the Contrastive Language-Image Pretraining (CLIP) objective are evaluated for evidence of a bias studied by psychologists…
American == White in Multimodal Language-and-Image AI
Robert Wolfe, Aylin Caliskan
Three state-of-the-art language-and-image AI models, CLIP, SLIP, and BLIP, are evaluated for evidence of a bias previously observed in social and experimental psychology: equating…
Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semantics
Aylin Caliskan, Pimparkar Parth Ajay, Tessa Charlesworth +2
The statistical regularities in language corpora encode well-known social biases into word embeddings. Here, we focus on gender to provide a comprehensive analysis of group-based b…
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…