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Tepper School Faculty Member Wins Amazon Research Award for AI Work

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Peter Kerwin
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University Communications & Marketing

Yan Huang(opens in new window), associate professor of business technologies at 온라인바카라’s Tepper School of Business(opens in new window), has won an for her work on fairness in artificial intelligence (AI).

Huang joins 78 other recipients of the award, which offers unrestricted funds and promotional credits to support research in multiple disciplines at academic institutions and nonprofit organizations. The awardees represent 54 universities in 14 countries.

“I am deeply honored to receive this award from Amazon for my work on bias in AI,” said Huang.

“Algorithmic bias is a growing concern. Since algorithms are often trained using data generated by humans, we need to determine how machine learning inherits different types of human bias and how to improve fairness from managerial and technical perspectives.”

The awards were given in four categories: (Amazon Web Services is part of Science at Amazon), , , and . Proposals were reviewed for the quality of their scientific content and their potential to affect the research community and society.

Huang won in the category of AWS AI for her joint work with , associate professor of IT and management at 바카라 온라인 추천’s ; Xiyang Hu, a Ph.D. student in information systems(opens in new window) at 바카라 온라인 추천; and Tian Lu, a former postdoc research fellow at Heinz College, now assistant professor of information systems at Arizona State University. Their work is titled, “Combating Algorithmic Bias Inherited from Human Decision Making: A Human-AI Perspective."

This project has also led to a working paper on SSRN that Huang coauthored with Hu, Li and Lu entitled,

“Dr. Huang was selected for this prestigious award from among the brightest minds in AI,” said Isabelle Bajeux-Besnainou(opens in new window), dean of the Tepper School.

“Amid the recent boom in new technologies, machine learning algorithms have replaced humans in making many important decisions,” Bajeaux-Besnainou said. “Understanding biases in AI is a crucial step in being able to correct them.”

Yan Huang

Yan Huang

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