By Tom Ventsias
Fully ingrained into our daily lives, artificial intelligence (AI) algorithms can help us shop online, calculate credit scores, navigate vehicles, and for those that run afoul of the law, offer judges criminal sentencing guidelines.
But as the use of AI increases exponentially, so does the concern that biased data can result in flawed decisions or prejudiced outcomes.
At the University of Maryland, two teams of researchers are helping to eliminate those biases by developing new algorithms and protocols that can improve the efficiency, reliability and trustworthiness of AI systems. Specifically, the Maryland faculty are working to improve fairness and accuracy in AI-based platforms used for college admissions and are rethinking traditional language translation systems to make them more user-friendly.
Their work is supported by a joint initiative called the NSF Program on Fairness in Artificial Intelligence in Collaboration with Amazon...
Carpuat’s team includes Ge Gao, an assistant professor in the iSchool, and Niloufar Salehi, an assistant professor in the School of Information at UC Berkeley.