Behind Twitter’s Biased AI Cropping and How to Fix It. A dive into the tech and practice that caused the bias in Twitter’s AI Crop and some suggested paths to recovery.
Twitter’s AI crop has a bias. When given a large photo that contained the press photos of Mitch McConnell and Barack Obama, the AI picks Mitch. Swap the position of the two pictures, and the AI picks the white guy again. This behavior has led to a lot of random experimentation online. Still, it’s important to cover what we know, what Twitter’s response was, what caused the problem, and how to fix it.
It’s a well-known fact that people like to click on stories with images. It’s also known that images that are uniform, consistent, and canonical get more clicks. For example, take this photo of the bent pyramid at Dahshur. First, I’ll give you a square center crop.
Are you likely to click on that? Not a chance. Let’s do another square crop, but this time around the salient object.
Boom, it’s click city. This effect gets amplified when it comes to faces. Years ago, Saeideh Bakhshi, Eric Gilbert, and I published findings that showed photos with people’s faces get more engagement on Instagram and Flickr. The Internet is full of tools trying to get better crops and has been for decades because better crops lead to better engagement and more dollars. In this case, it will come down to the Image and the AI.
Crop image before upload in laravel. Here, we will show how to crop image before upload in DB & folder using jquery cropper js in laravel.
In this post you can find how to use Cropper.js library for Crop image before uploading of image to the server using PHP script. In this tut...
This article is an introduction to abstraction in general. The content presented here applies to several sciences other than software engineering.
Everyone is prey to cognitive biases that skew thinking, but data scientists must prevent them from spoiling their work. Learn more about five biases that can all too easily make your seemingly objective work become surprisingly subjective.
Those interested in studying AI bias, but who lack a starting point, would do well to check out this introductory set of slides and the accompanying talk on the subject from Google researcher Margaret Mitchell.