Social (Net)Work: What can A.I. catch — and where does it fail miserably?

What can A.I. catch

From a video of a suicide victim on YouTube to ads targeting “Jew haters,” on Facebook, social media platforms are plagued by inappropriate content that manages to slip through the cracks. In many cases, the platform’s response is to implement smarter algorithms to better identify inappropriate content. But what is artificial intelligence really capable of catching, how much should we trust it, and where does it fail miserably?

“A.I. can pick up offensive language and it can recognize images very well. The power of identifying the image is there,” says Winston Binch, the chief digital officer of Deutsch, a creative agency that uses A.I. in creating digital campaigns for brands from Target to Taco Bell. “The gray area becomes the intent.”

A.I. can read both text and images, but accuracy varies

Using natural language processing, A.I. can be trained to recognize text across multiple languages. A program designed to spot posts that violate community guidelines, for example, can be taught to detect racial slurs or terms associated with extremist propaganda.

A.I. can also be trained to recognize images, to prevent some forms of nudity or recognize symbols like the swastika. It works well in many cases, but it isn’t foolproof. For example, Google Photos was criticized for tagging images of dark-skinned people with the keyword “gorilla.” Years later, Google still hasn’t found a solution for the problem, instead choosing to remove the program’s ability to tag monkeys and gorillas entirely.

Algorithms also need to be updated as a word’s meaning evolves, or to understand how a word is used in context. For example, LGBT Twitter users recently noticed a lack of search results for #gay and #bisexual, among other terms, leading some to feel the service was censoring them. Twitter apologized for the error, blaming it on an outdated algorithm that was falsely identifying posts tagged with the terms as potentially offensive. Twitter said its algorithm was supposed to consider the term in the context of the post, but had failed to do so with those keywords.

A.I. is biased

The gorilla tagging fail brings up another important shortcoming — A.I. is biased. You might wonder how a computer could possibly be biased, but A.I. is trained by watching people complete tasks, or by inputting the results of those tasks. For example, programs to identify objects in a photograph are often trained by feeding the system thousands of images that were initially tagged by hand.

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Article Credit: Digital Trends

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