Aylin Caliskan
Nonresident Fellow - Governance Studies, Center for Technology Innovation
Aylin Caliskan is a Nonresident Fellow in Governance Studies at The Brookings Institution, housed in the Center for Technology Innovation. Caliskan also serves as an assistant professor at the University of Washington's Information School. Caliskan's research interests lie in artificial intelligence (AI) ethics, bias in AI, machine learning, and the implications of machine intelligence on privacy and equity. She investigates the reasoning behind biased AI representations and decisions by developing theoretically grounded statistical methods that uncover and quantify the biases of machines. Building these transparency enhancing algorithms involves the use of machine learning, natural language processing, and computer vision to interpret AI and gain insights about bias in machines as well as society. Caliskan's publication in Science demonstrated how semantics derived from language corpora contain human-like biases. Their work on machine learning's impact on individuals and society received the best talk and best paper awards. Caliskan was selected as a Rising Star in EECS at Stanford University. Caliskan holds a Ph.D. in Computer Science from Drexel University's College of Computing & Informatics and a Master of Science in Robotics from the University of Pennsylvania. Caliskan was a Postdoctoral Researcher and a Fellow at Princeton University's Center for Information Technology Policy.
Aylin Caliskan is a Nonresident Fellow in Governance Studies at The Brookings Institution, housed in the Center for Technology Innovation. Caliskan also serves as an assistant professor at the University of Washington’s Information School. Caliskan’s research interests lie in artificial intelligence (AI) ethics, bias in AI, machine learning, and the implications of machine intelligence on privacy and equity. She investigates the reasoning behind biased AI representations and decisions by developing theoretically grounded statistical methods that uncover and quantify the biases of machines. Building these transparency enhancing algorithms involves the use of machine learning, natural language processing, and computer vision to interpret AI and gain insights about bias in machines as well as society. Caliskan’s publication in Science demonstrated how semantics derived from language corpora contain human-like biases. Their work on machine learning’s impact on individuals and society received the best talk and best paper awards. Caliskan was selected as a Rising Star in EECS at Stanford University. Caliskan holds a Ph.D. in Computer Science from Drexel University’s College of Computing & Informatics and a Master of Science in Robotics from the University of Pennsylvania. Caliskan was a Postdoctoral Researcher and a Fellow at Princeton University’s Center for Information Technology Policy.
