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Auditing algorithms for bias

So much for the idea that bots will be taking over human jobs. Once we have AIs doing work for us, we'll need to invent new jobs for humans who are testing the AIs' results for accuracy and prejudice. Even when chatbots get incredibly sophisticated, they are still going to be trained on human language. And since bias is built into language, humans will still be necessary as decision-makers.

In a recent paper for Science about their work, the researchers say the implications are far-reaching. "Our findings are also sure to contribute to the debate concerning the Sapir Whorf hypothesis," they write. "Our work suggests that behavior can be driven by cultural history embedded in a term's historic use. Such histories can evidently vary between languages." If you watched the movie Arrival, you've probably heard of Sapir Whorf--it's the hypothesis that language shapes consciousness. Now we have an algorithm that suggests this may be true, at least when it comes to stereotypes.

Aylin Caliskan said her team wants to branch out and try to find as-yet-unknown biases in human language. Perhaps they could look for patterns created by fake news or look into biases that exist in specific subcultures or geographical locations. They would also like to look at other languages, where bias is encoded very differently than it is in English.

"Let's say in the future, someone suspects there's a bias or stereotype in a certain culture or location," Caliskan mused. "Instead of testing with human subjects first, which takes time, money, and effort, they can get text from that group of people and test to see if they have this bias. It would save so much time."

See also Princeton Researchers discover AI bias and
Science, 2017. DOI: 10.1126/science.aal4230


Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan1,*, Joanna J. Bryson1,2,*, Arvind Narayanan1,*
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Science 14 Apr 2017:
Vol. 356, Issue 6334, pp. 183-186
DOI: 10.1126/science.aal4230


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