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Experts Warn Biases Must Be Removed From Artificial Intelligence

CNN Tech reported:

"Every time humanity goes through a new wave of innovation and technological transformation, there are people who are hurt and there are issues as large as geopolitical conflict," said Fei Fei Li, the director of the Stanford Artificial Intelligence Lab. "AI is no exception." These are not issues for the future, but the present. AI powers the speech recognition that makes Siri and Alexa work. It underpins useful services like Google Photos and Google Translate. It helps Netflix recommend movies, Pandora suggest songs, and Amazon push products..."

Artificial intelligence (AI) technology is not only about autonomous ships, trucks, and preventing crashes involving self-driving cars. AI has global impacts. Researchers have already identified problems and limitations:

"A recent study by Joy Buolamwini at the M.I.T. Media Lab found facial recognition software has trouble identifying women of color. Tests by The Washington Post found that accents often trip up smart speakers like Alexa. And an investigation by ProPublica revealed that software used to sentence criminals is biased against black Americans. Addressing these issues will grow increasingly urgent as things like facial recognition software become more prevalent in law enforcement, border security, and even hiring."

Reportedly, the concerns and limitations were discussed earlier this month at the "AI Summit - Designing A Future For All" conference. Back in 2016, TechCrunch listed five unexpected biases in artificial intelligence. So, there is much important work to be done to remove biases.

According to CNN Tech, a range of solutions are needed:

"Diversifying the backgrounds of those creating artificial intelligence and applying it to everything from policing to shopping to banking...This goes beyond diversifying the ranks of engineers and computer scientists building these tools to include the people pondering how they are used."

Given the history of the internet, there seems to be an important take-away. Early on, many people mistakenly assumed that, "If it's in an e-mail, then it must be true." That mistaken assumption migrated to, "If it's in a website on the internet, then it must be true." And that mistaken assumption migrated to, "If it was posted on social media, then it must be true." Consumers, corporate executives, and technicians must educate themselves and avoid assuming, "If an AI system collected it, then it must be true." Veracity matters. What do you think?

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