Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

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

Association for Computing Machinery (ACM) via YouTube

Overview

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.

Syllabus

Introduction
Implicit Association Test
Methods
Results
Key Observations
Image Generation
What Next

Taught by

ACM FAccT Conference

Reviews

Start your review of Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.