Star American Professor Masterminded a Surveillance Machine For Chinese Big Tech (thedailybeast.com) 26
An anonymous reader quotes a report from The Daily Beast: A star University of Maryland (UMD) professor built a machine-learning software "useful for surveillance" as part of a six-figure research grant from Chinese tech giant Alibaba, raising concerns that an American public university directly contributed to China's surveillance state. Alibaba provided $125,000 in funding to a research team led by Dinesh Manocha, a professor of computer science at UMD College Park, to develop an urban surveillance software that can "classify the personality of each pedestrian and identify other biometric features," according to research grant documents obtained via public records request. "These capabilities will be used to predict the behavior of each pedestrian and are useful for surveillance," the document read.
Manocha is a decorated scholar in the AI and robotics field who has earned awards and accolades from Google, IBM, and many others. His star status brings rewards: Maryland taxpayers paid $355,000 in salaries to the professor in 2021, according to government watchdog Open the Books. The U.S. military also provides lavish funding for the professor's research, signing a $68 million agreement with Manocha's lab to research military applications of AI technologies. But Maryland taxpayers and the U.S. military are not the only ones funding Manocha's research. In January 2018, the University of Maryland and Alibaba signed an 18-month research contract funding Manocha's research team. In the grant document obtained by The Daily Beast, Manocha's team pledged to "work closely with Alibaba researchers" to develop an urban surveillance software that can identify pedestrians based on their unique gait signatures. The algorithm would then use the gait signatures to classify pedestrians as "aggressive," "shy," "impulsive," and other personalities. The grant required UMD researchers to test the algorithm on videos provided by Alibaba and present their findings in person at Alibaba labs in China. The scholars also had to provide the C++ codebase for the software and the raw dataset as deliverables to Alibaba. The software's "clear implication is to proactively predict demonstrations and protests so that they might be quelled," Fedasiuk told The Daily Beast. "Given what we know now about China's architecture of repression in Xinjiang and other regions, it is clear Dr. Manocha should not have pitched this project, and administrators at UMD should not have signed off on it."
It's not just Alibaba that was interested in the professor's expertise. In January 2019 -- back when the Alibaba grant was still active -- Manocha secured a taxpayer-funded, $321,000 Defense Department grant for his research team. The two grants funded very similar research projects. The Alibaba award was titled "large-scale behavioral learning for dense crowds." Meanwhile, the DoD grant funded research into "efficient computational models for simulating large-scale heterogeneous crowds." Unsurprisingly, the research outputs produced by the two grants had significant overlap. Between 2019 and 2021, Manocha published multiple articles in the AI and machine-learning field that cited both the Alibaba and DoD grant. There is no evidence that Manocha broke the law by double-dipping from U.S. and Chinese funding sources to fund similar research projects. Nevertheless, the case still raises "serious questions about ethics in machine learning research," Fedasiuk said.
Manocha is a decorated scholar in the AI and robotics field who has earned awards and accolades from Google, IBM, and many others. His star status brings rewards: Maryland taxpayers paid $355,000 in salaries to the professor in 2021, according to government watchdog Open the Books. The U.S. military also provides lavish funding for the professor's research, signing a $68 million agreement with Manocha's lab to research military applications of AI technologies. But Maryland taxpayers and the U.S. military are not the only ones funding Manocha's research. In January 2018, the University of Maryland and Alibaba signed an 18-month research contract funding Manocha's research team. In the grant document obtained by The Daily Beast, Manocha's team pledged to "work closely with Alibaba researchers" to develop an urban surveillance software that can identify pedestrians based on their unique gait signatures. The algorithm would then use the gait signatures to classify pedestrians as "aggressive," "shy," "impulsive," and other personalities. The grant required UMD researchers to test the algorithm on videos provided by Alibaba and present their findings in person at Alibaba labs in China. The scholars also had to provide the C++ codebase for the software and the raw dataset as deliverables to Alibaba. The software's "clear implication is to proactively predict demonstrations and protests so that they might be quelled," Fedasiuk told The Daily Beast. "Given what we know now about China's architecture of repression in Xinjiang and other regions, it is clear Dr. Manocha should not have pitched this project, and administrators at UMD should not have signed off on it."
It's not just Alibaba that was interested in the professor's expertise. In January 2019 -- back when the Alibaba grant was still active -- Manocha secured a taxpayer-funded, $321,000 Defense Department grant for his research team. The two grants funded very similar research projects. The Alibaba award was titled "large-scale behavioral learning for dense crowds." Meanwhile, the DoD grant funded research into "efficient computational models for simulating large-scale heterogeneous crowds." Unsurprisingly, the research outputs produced by the two grants had significant overlap. Between 2019 and 2021, Manocha published multiple articles in the AI and machine-learning field that cited both the Alibaba and DoD grant. There is no evidence that Manocha broke the law by double-dipping from U.S. and Chinese funding sources to fund similar research projects. Nevertheless, the case still raises "serious questions about ethics in machine learning research," Fedasiuk said.