Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
Six Ways Machine Learning Threatens Social Justice
 Originally published in Big Think When you harness the...
Transitions: Predicting The Next Event
 Models predicting the potential spread of the COVID-19 pandemic...
Coursera’s “Machine Learning for Everyone” Fulfills Unmet Training Requirements
  My new course series on Coursera, Machine Learning...
Segmentation and RFM Analysis in the World of Wine and Spirits
 Segmentation is a hot word these days, and it...
SHARE THIS:

4 months ago
What a Machine Learning Tool That Turns Obama White Can (And Can’t) Tell Us About AI Bias

 
Originally published in The Verge, June 23, 2020

A striking image that only hints at a much bigger problem.

It’s a startling image that illustrates the deep-rooted biases of AI research. Input a low-resolution picture of Barack Obama, the first black president of the United States, into an algorithm designed to generate depixelated faces, and the output is a white man.

It’s not just Obama, either. Get the same algorithm to generate high-resolution images of actress Lucy Liu or congresswoman Alexandria Ocasio-Cortez from low-resolution inputs, and the resulting faces look distinctly white. As one popular tweet quoting the Obama example put it: “This image speaks volumes about the dangers of bias in AI.”

But what’s causing these outputs and what do they really tell us about AI bias?

First, we need to know a little a bit about the technology being used here. The program generating these images is an algorithm called PULSE, which uses a technique known as upscaling to process visual data. Upscaling is like the “zoom and enhance” tropes you see in TV and film, but, unlike in Hollywood, real software can’t just generate new data from nothing. In order to turn a low-resolution image into a high-resolution one, the software has to fill in the blanks using machine learning.

To continue reading this article click here.