Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases

Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases

Explore a 20-minute conference talk from the FAccT 2021 virtual event that delves into the human-like biases present in image representations learned through unsupervised pre-training. Presented by R. Steed and A. Caliskan, this research-focused presentation covers the Implicit Association Test, methodologies employed, and key findings. Gain insights into image generation techniques and future directions in this field. Understand how unsupervised machine learning models can inadvertently incorporate societal biases, mirroring human prejudices in visual representations.