Context reduces racial bias in hate speech detection algorithms Date: July 7, 2020 Source: University of Southern California Summary: When it comes to accurately flagging hate speech on social ...

They found the single algorithm that included three categories—hate speech, offensive speech, and ordinary speech—as opposed to two, did a better job of avoiding false positives.

The algorithm uses neural networks, more popularly called as deep learning. These algorithms are inspired from the human brain, and they try to simulate how humans learn from examples.

O n Nov. 13, Facebook announced with great fanfare that it was taking down substantially more posts containing hate speech from its platform than ever before.. Facebook removed more than seven ...

Instead, we found widespread bias in a variety of hate speech detection datasets, which if you train machine learning models on them, those models will be biased against African American English ...

Hate speech reported by users and others -- including against white people, Americans and men -- will still be removed. Facebook uses a mix of human reviewers and technology to remove harmful content.

Image and video recognition – Systems for recognizing images and videos classify and analyze visual data using deep learning algorithms. Security systems, medical imaging, and self-driving ...

"Our group of Black Lives Matter activists actually met with Facebook representatives in February 2016, not 2014 as it says in The Washington Post article, to discuss the appalling levels of ...

The algorithms that detect hate speech online are biased against black people A new study shows that leading AI models are 1.5 times more likely to flag tweets written by African Americans as ...

Researchers from Finland’s Aalto University have analyzed various anti-hate speech systems, including tools built by Google’s Counter Abuse team. Their findings demonstrate that the technology ...