Racism in the Algorithms behind Artificial Intelligence

racism fpga.PNGDo a quick google search for ‘babies.’

Do another quick google search for ‘hands.’

Chances are, most of the pictures you see a predominately Caucasian. And that’s what the problem is. When the algorithms behind a web search pulls pictures, they are retrieving images that are used the most often.

It has reached the point where Google had to defend its image search results stating the search results are “a reflection of content from across the web, including the frequency with which types of images appear and the way they’re described online” and are not connected to its “values.”

Back in 2015, graphic designer Johanna Burai created what she termed as the White Web Project. The project offers alternative options to balance out what’s currently available through some of the more popular search engines.

Artificial Intelligence was having trouble recognizing diversity, but now, it has gotten better. The problem used to be the majority of tools, including pictures and videos, used to train Artifical Intelligence contained very few minorities.

In November 2016, MIT student Joy Buolamwini launched the Algorithmic Justice League (AJL), a project to eradicate bias in artificial intelligence. The interest in the project has been enthusiastic and varied.

Teachers use it as an example for students. Some developers are interested in relying on the project to make sure their algorithms are free from bias. The great thing about the algorithm is that it doesn’t make a case just for racism – the algorithm considers other factors in the tech world, including sexism, ageism, and different body shapes.

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Sources used:


Digital Trends


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