AI News, AI Learns Gender and Racial Biases From Language
- On Saturday, February 17, 2018
- By Read More
AI Learns Gender and Racial Biases From Language
Artificial intelligence does not automatically rise above human biases regarding gender and race.
On the contrary, machine learning algorithms that represent the cutting edge of AI in many online services and apps may readily mimic the biases encoded in their training datasets.
'In all cases where machine learning aids in perceptual tasks, the worry is that if machine learning is replicating human biases, it's also reflecting that back at us,' says Arvind Narayanan, a computer scientist at the Center for Information Technology Policy in Princeton University.
To reveal the biases that can arise in natural language learning, Narayanan and his colleagues created new statistical tests based on the Implicit Association Test (IAT) used by psychologists to reveal human biases.
Their work detailed in the 14 April 2017 issue of the journal Science is the first to show such human biases in 'word embedding'—a statistical modeling technique commonly used in machine learning and natural language processing.
To understand the possible implications, one only need look at the Pulitzer Prize finalist 'Machine Bias' series by ProPublica that showed how a computer program designed to predict future criminals is biased against black people.
The new study takes an important step forward by revealing possible language biases within a broad category of machine learning, says Sorelle Friedler, a computer scientist at Haverford College who was not involved with the latest study.
As an organizer of the Workshop on Fairness, Accountability, and Transparency in Machine Learning, Friedler pointed out that past studies have mainly examined the biases of specific machine learning algorithms already 'live' and performing services in the real world.
People will need to make tough ethical calls on what bias looks like and how to proceed from there, lest they allow such biases to run unchecked within increasingly powerful and widespread AI systems.
- On Tuesday, September 17, 2019
Machine Learning and Human Bias
As researchers and engineers, our goal is to make machine learning technology work for everyone.
But what *is* a Neural Network? | Chapter 1, deep learning
Subscribe to stay notified about new videos: Support more videos like this on Patreon: Special thanks to these supporters:
Machine Learning & Artificial Intelligence: Crash Course Computer Science #34
So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical...
The 7 Steps of Machine Learning
How can we tell if a drink is beer or wine? Machine learning, of course! In this episode of Cloud AI Adventures, Yufeng walks through the 7 steps involved in applied machine learning. The...
What is a Neural Network - Ep. 2 (Deep Learning SIMPLIFIED)
With plenty of machine learning tools currently available, why would you ever choose an artificial neural network over all the rest? This clip and the next could open your eyes to their awesome...
Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka
TensorFlow Training - ) This Edureka "Neural Network Tutorial" video (Blog: will help you to understand the.
Why AI will probably kill us all.
When you look into it, Artificial Intelligence is absolutely terrifying. Really hope we don't die. ▻ ▻ If you want to support what I do, this is the best way:
Introduction to Artificial Intelligence | Deep Learning | Edureka
TensorFlow Training - ) This video on Artificial intelligence gives you an introduction to artificial intelligence with futuristic..
Deep Learning Tutorial | Deep Learning Tutorial for Beginners | Neural Networks | Edureka
Deep Learning Training - ) This Edureka "Deep Learning Tutorial" video (Blog: will help you to understand about.
How good is your fit? - Ep. 21 (Deep Learning SIMPLIFIED)
A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to generalize. A model...