Can AI be biased? Yes. And that’s because of us.

When it comes to programming our artificial intelligence machines, the data we humans use may be making them biased.

“As machines are trained on human data, they also learn human culture and effects about the world, and bias is part of this,” explains Aylin Caliskan, a ‎postdoctoral research associate at Princeton University.

Caliskan coauthored a paper for Science discussing how AI systems pick up on human biases through the material we train them with. While we teach our AI to understand our language, the language given is also embedded with its own biases.

The first test of identifying biases involved flowers, insects, instruments and weapons. Caliskan and her team found that the AI machine, which should be neutral, began developing biases. It showed biased towards flowers and instruments. And developed a bias against insects and weapons. Their next tests focused on gender, age, and race.

In a conversation with tech journalist Michael Copeland in the latest episode of our AI Podcast, Caliskan discussed potential problems she foresaw with this outcome, and how it could affect future AI-driven applications, such as resume screening.

“Whenever language is a part of a program or an application — and we can think about for example web search, autofill, machine translation, or anything that involves text — this association will somehow be reflected in the output of these applications or programs,” Caliskan says.

But she remains optimistic, suggesting AI could even help us recognize our own biases.

“We see the example that even language is able to reflect bias. So, that should maybe make us think about some questions,” Caliskan says. “Just like the frequency and the places we encounter things may be causing certain associations for us even though we don’t realize that these things are happening. So, I guess we need to always try to be aware to see if bias exists or not. And machines can help us do this.”

To hear more about bias in AI, tune into the latest edition of NVIDIA’s AI Podcast.

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Image credit, featured image: Michael Coghlan, via Flickr.