2.9k citations · 3.9k across the 22 of their papers we have counts for
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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…
Disparate Impact of Artificial Intelligence Bias in Ridehailing Economy's Price Discrimination Algorithms
Akshat Pandey, Aylin Caliskan
Ridehailing applications that collect mobility data from individuals to inform smart city planning predict each trip's fare pricing with automated algorithms that rely on artificia…
Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases
Wei Guo, Aylin Caliskan
With the starting point that implicit human biases are reflected in the statistical regularities of language, it is possible to measure biases in English static word embeddings. St…
ValNorm Quantifies Semantics to Reveal Consistent Valence Biases Across Languages and Over Centuries
Autumn Toney-Wails, Aylin Caliskan
Word embeddings learn implicit biases from linguistic regularities captured by word co-occurrence statistics. By extending methods that quantify human-like biases in word embedding…
Automatically Characterizing Targeted Information Operations Through Biases Present in Discourse on Twitter
Autumn Toney, Akshat Pandey, Wei Guo +2
This paper considers the problem of automatically characterizing overall attitudes and biases that may be associated with emerging information operations via artificial intelligenc…
A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the Wild
Ryan Steed, Aylin Caliskan
Research in social psychology has shown that people's biased, subjective judgments about another's personality based solely on their appearance are not predictive of their actual p…