New applications of machine learning are raising ethical concerns about a host of issues, including bias, transparency, and privacy. Iman Saleh, Cory Ilo, and Cindy Tseng demonstrate tools and capabilities that can help data scientists address these concerns and bridge the gap between ethicists, regulators, and machine learning practitioners.
Iman Saleh is a research scientist with the Automotive Solutions Group at Intel. Iman has authored 30+ technical publications in the areas of big data, formal data specification, service-oriented computing, and privacy-preserving data mining. Her research interests include ethical AI, machine learning, privacy-preserving solutions, software engineering, data modeling, web services, formal methods, and cryptography. She holds a PhD from the Computer Science Department at Virginia Tech, a master’s degree in computer science from Alexandria University, Egypt, and a master’s degree in software engineering from Virginia Tech.
Cory Ilo is a computer vision engineer in the Automotive Solutions Group at Intel, where he helps prototype and research the feasibility of various computer vision solutions in relation to privacy, ethics, deep learning, and autonomous vehicles. In his spare time, Cory focuses on his passion for fitness, video games, and wanderlust, in addition to finding ways on how they tie into computer vision.
Cindy Tseng is a research scientist with the Applied Research in Automotive Driving Group at Intel, where she has recently been focusing on bias detection in convolution neural nets. Cindy has also worked in the high-throughput computing and deep learning hardware accelerator spaces. She holds a master’s degree from the Electrical and Computer Engineering Department at Carnegie Mellon University and a bachelor’s degree in electrical engineering and computer science from the University of Michigan-Ann Arbor. Cindy is currently enrolled as a part-time student in the Masters in Data Science Program in Computer Science at the University of Illinois Urbana-Champaign.
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