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Inhuman Error: How Do We Counteract AI Bias?

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rolled out at the end of last year, it thrust a conversation that has mostly happened in tech circles for decades into the public eye.Academics panicked at the thought of students submitting papers written entirely by AI, tech companies rushed to quickly push out their own AI chatbots and small-business owners and entrepreneurs behind the curve were suddenly faced with incorporating machine-learning technologies (which comprise most of what we call artificial intelligence) into their day-to-day business practices.Here’s the thing: is not intelligent.

Machine-learning applications “learn” by recognizing complex patterns in data. What they are really doing is using algorithms to predict what makes the most sense based on the data used to train them.

What this means, unfortunately, is that human biases end up in the applications.The most famous case of this happened in 2018, when because it was discriminating against women.

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