AI News, AI Learns Gender and Racial Biases From Language
- On Saturday, February 17, 2018
- By Read More
AI Learns Gender and Racial Biases From Language
Artificial intelligence does not automatically rise above human biases regarding gender and race.
On the contrary, machine learning algorithms that represent the cutting edge of AI in many online services and apps may readily mimic the biases encoded in their training datasets.
'In all cases where machine learning aids in perceptual tasks, the worry is that if machine learning is replicating human biases, it's also reflecting that back at us,' says Arvind Narayanan, a computer scientist at the Center for Information Technology Policy in Princeton University.
To reveal the biases that can arise in natural language learning, Narayanan and his colleagues created new statistical tests based on the Implicit Association Test (IAT) used by psychologists to reveal human biases.
Their work detailed in the 14 April 2017 issue of the journal Science is the first to show such human biases in 'word embedding'—a statistical modeling technique commonly used in machine learning and natural language processing.
To understand the possible implications, one only need look at the Pulitzer Prize finalist 'Machine Bias' series by ProPublica that showed how a computer program designed to predict future criminals is biased against black people.
The new study takes an important step forward by revealing possible language biases within a broad category of machine learning, says Sorelle Friedler, a computer scientist at Haverford College who was not involved with the latest study.
As an organizer of the Workshop on Fairness, Accountability, and Transparency in Machine Learning, Friedler pointed out that past studies have mainly examined the biases of specific machine learning algorithms already 'live' and performing services in the real world.
People will need to make tough ethical calls on what bias looks like and how to proceed from there, lest they allow such biases to run unchecked within increasingly powerful and widespread AI systems.
- On Sunday, January 20, 2019
Machine Learning and Human Bias
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