Aylin Caliskan is an Assistant Professor of Computer Science at George Washington University. Her research interests lie in artificial intelligence (AI) ethics, bias in AI, machine learning, and the implications of machine intelligence on equity and fairness. She investigates the reasoning behind biased AI representations and decisions by developing theoretically grounded statistical methods that uncover and quantify 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. Her publication in the Science Magazine demonstrated how semantics derived from language corpora contain human-like biases. Her work on machine learning's impact on individuals and society received the best talk and best paper awards. She has been selected as a Rising Star in EECS at Stanford University. Aylin 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. She was a Postdoctoral Researcher and a Fellow at Princeton University's Center for Information Technology Policy.