Monitoring for offensive content takes a toll on humans. AI and ML can shoulder much of the burden, benefiting individuals and organizations alike.
copyright by www.itprotoday.com
The human cost of monitoring for offensive content from social networks and other sites can be high, but advances in artificial intelligence and machine learning are making it possible to offload that work (some of it, anyway), benefiting individuals and organizations alike.
In 2014, Wired ran an article about the emotional and psychological toll taken on people in the Philippines who work to remove offensive content in their roles as content moderators for social media platforms like Facebook. In March of this year, the BBC’s Storyville ran an episode called ” The Internet’s Dirtiest Secrets: The Cleaners .” Like the Wired article, the BBC documentary focused on the terrible toll that filtering the worst of the internet takes on humans.
LinkedIn and other companies ( and countries ) may have hit on a way to mitigate that cost: machine learning. Indeed, ML and AI have become valuable tools for purposes not previously predicted, such as preventing certain content on professional platforms, says
Joana Gutierrez, the CEO and founder of the virtual assistant Meethappy .
The task of keeping nudity and profanity–and much worse–off LinkedIn and other internet platforms isn’t an easy one. LinkedIn has well over half a billion members who post in a couple dozen different languages. If the social network is going to rely on machine learning to keep the site clean, it has to work really well.
Fortunately for LinkedIn, it does. That’s thanks to an algorithm made by Rushi Bhatt, who works in Bengaluru, India. Factor Daily reported on Bhatt’s work, which happens at the company’s development center in Bengaluru and is tied to the site’s ongoing focus on user-generated content. For the Factor Daily article, Bhatt described how machine learning is used to identify the quality of content for LinkedIn, including categorizing content and pre-processing images. The ability to rely on machine learning for this work has expanded in the time since Wired reported on Filipino content moderators, thanks to the advancement in neural network technology.
The benefits of such use don’t come only with content created outside a company. They can also save a firm from an HR (and PR) nightmare if an employee were to post inappropriate material on a company feed, either intentionally or accidentally, Gutierrez pointed out.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
There are more advancements to come. For example, IBM’s Watson Tone Analyzer looks at what content intends as well as what it specifically says–an important tool for sites trying to balance freedom of speech with protection of users and removal of illegal or harmful content.[…]
read more – copyright by www.itprotoday.com
Monitoring for offensive content takes a toll on humans. AI and ML can shoulder much of the burden, benefiting individuals and organizations alike.
copyright by www.itprotoday.com
In 2014, Wired ran an article about the emotional and psychological toll taken on people in the Philippines who work to remove offensive content in their roles as content moderators for social media platforms like Facebook. In March of this year, the BBC’s Storyville ran an episode called ” The Internet’s Dirtiest Secrets: The Cleaners .” Like the Wired article, the BBC documentary focused on the terrible toll that filtering the worst of the internet takes on humans.
LinkedIn and other companies ( and countries ) may have hit on a way to mitigate that cost: machine learning. Indeed, ML and AI have become valuable tools for purposes not previously predicted, such as preventing certain content on professional platforms, says
Joana Gutierrez, the CEO and founder of the virtual assistant Meethappy .
The task of keeping nudity and profanity–and much worse–off LinkedIn and other internet platforms isn’t an easy one. LinkedIn has well over half a billion members who post in a couple dozen different languages. If the social network is going to rely on machine learning to keep the site clean, it has to work really well.
Fortunately for LinkedIn, it does. That’s thanks to an algorithm made by Rushi Bhatt, who works in Bengaluru, India. Factor Daily reported on Bhatt’s work, which happens at the company’s development center in Bengaluru and is tied to the site’s ongoing focus on user-generated content. For the Factor Daily article, Bhatt described how machine learning is used to identify the quality of content for LinkedIn, including categorizing content and pre-processing images. The ability to rely on machine learning for this work has expanded in the time since Wired reported on Filipino content moderators, thanks to the advancement in neural network technology.
The benefits of such use don’t come only with content created outside a company. They can also save a firm from an HR (and PR) nightmare if an employee were to post inappropriate material on a company feed, either intentionally or accidentally, Gutierrez pointed out.
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
There are more advancements to come. For example, IBM’s Watson Tone Analyzer looks at what content intends as well as what it specifically says–an important tool for sites trying to balance freedom of speech with protection of users and removal of illegal or harmful content.[…]
read more – copyright by www.itprotoday.com
Share this: