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Home » Twitter to investigate why its photo preview favors white faces over Black faces
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Twitter to investigate why its photo preview favors white faces over Black faces

Kshitij ThakurBy Kshitij ThakurSeptember 21, 2020Updated:September 21, 2020No Comments2 Mins Read
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Twitter to investigate why its photo preview favors white faces over Black faces
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Twitter said Sunday that it was looking into why the neural network it uses to select which part of an image to show in photo previews apparently chooses to show white people’s faces more frequently than Black people faces.

Twitter Users who noticed this over the weekend

Several users demonstrated these issues over the weekend, by posting the examples of posts that had a Black person’s face and a white person’s face. And frequently it’s preview showed the white faces more often.

Trying a horrible experiment…

Which will the Twitter algorithm pick: Mitch McConnell or Barack Obama? pic.twitter.com/bR1GRyCkia

— Tony "Abolish ICE" Arcieri 🦀🌹 (@bascule) September 19, 2020

The informal testing began after one user posted about a few problems he noticed in Zoom’s face recognition, which wasn’t showing the face of Black colleague on calls. And also when he posted it on Twitter, he noticed it was too favoring his white colleague’s face over his Black colleague’s face.

Turns out @zoom_us has a crappy face-detection algorithm that erases black faces…and determines that a nice pale globe in the background must be a better face than what should be obvious.

— Colin Madland 🇺🇦 (@colinmadland) September 19, 2020

Other user discovered the preview algorithm chose non-Black cartoon characters as well.

https://twitter.com/_jsimonovski/status/1307542747197239296?ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E1307542747197239296%7Ctwgr%5Eshare_3&ref_url=https%3A%2F%2Fwww.theverge.com%2F2020%2F9%2F20%2F21447998%2Ftwitter-photo-preview-white-black-faces

Twitter’s take on this situation

Twitter firstly began using the neural network to automatically crop photo previews, machine learning researchers explained during a blog post how they started with facial recognition to crop images, but found it is lacking, mainly because not all images have faces –

Previously, we used face detection to focus the view on the most prominent face we could find. While this is not an unreasonable heuristic,

the approach has obvious limitations since not all images contain faces. Additionally, our face detector often missed faces and sometimes mistakenly detected faces when there were none. If no faces were found, we would focus the view on the center of the image.

This could lead to awkwardly cropped preview images.

And when the general public tests got the company’s attention. Twitter’s chief design officer Dantley Davis tweeted that the company was investigating the neural network, and also conducted some experiments with images –

Here's another example of what I've experimented with. It's not a scientific test as it's an isolated example, but it points to some variables that we need to look into. Both men now have the same suits and I covered their hands. We're still investigating the NN. pic.twitter.com/06BhFgDkyA

— Dantley Davis (@dantley) September 20, 2020

Based on some experiments I tried, I think @colinmadland's facial hair is affecting the model because of the contrast with his skin. I removed his facial hair and the Black man shows in the preview for me. Our team did test for racial bias before shipping the model. pic.twitter.com/Gk33NQlGgB

— Dantley Davis (@dantley) September 19, 2020

Liz Kelley of the Twitter communications team tweeted on Sunday that –

thanks to everyone who raised this. we tested for bias before shipping the model and didn't find evidence of racial or gender bias in our testing, but it’s clear that we’ve got more analysis to do. we'll open source our work so others can review and replicate. https://t.co/E6sZV3xboH

— liz kelley (@lizkelley) September 20, 2020

Company’s chief technology officer Parag Agrawal tweeted that –

This is a very important question. To address it, we did analysis on our model when we shipped it, but needs continuous improvement.

Love this public, open, and rigorous test — and eager to learn from this. https://t.co/E8Y71qSLXa

— Parag Agrawal (@paraga) September 20, 2020

Twitter’s promise to research is basically encouraging, but every user should view these analyses with a grain of salt. It’s problematic to say incidences of bias from a couple of examples. To actually assess bias, researchers need an outsized sample size with multiple examples under a spread of circumstances.

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Kshitij Thakur

The heavy Sniper, Kshitij is the marksman of the team Craffic. He joined the team in 2018 and his continuous hard work and dedication to the work has made his precision in work unmatched. Kshitij has experience in editing the work of others to foster stronger bonds with fellow authors and working together to improve each other's work.

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