Aylin Caliskan is an Assistant Professor at the University of Washington Information School and an Adjunct Assistant Professor at the Paul G. Allen School of Computer Science and Engineering. Caliskan studies implicit machine cognition to uncover the underpinning mechanisms of information transfer from human society to artificial intelligence (AI). Specifically, Caliskan's research interests lie in AI ethics, AI bias, computer vision, natural language processing, and machine learning. By developing computational statistical methods that detect and quantify human-like associations and biases learned by machines, Caliskan investigates the reasoning behind AI representations and decisions. Caliskan's publication in Science demonstrated how semantics derived from language corpora contain human-like biases. Her work on machine learning's impact on fairness and privacy received the best talk and best paper awards, and she 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. In 2021, Caliskan was named a Nonresident Fellow in Governance Studies at the Brookings Institution.

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