Aylin Caliskan
Aylin Caliskan is an assistant professor in the Information School at the University of Washington. 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.

Ryan Cordell

Ryan Cordell is Associate Professor in the School of Information Sciences at the University of Illinois, Urbana—Champaign. His scholarship seeks to illuminate how technologies of production, reception, circulation, and remediation shape the sociology of texts. Cordell primarily studies circulation and reprinting in nineteenth-century American newspapers, but his interests extend to the influence of computation and digitization on contemporary reading, writing, and research. Cordell collaborates with colleagues in English, History, and Computer Science on the Viral Texts project, which uses robust data mining tools to discover borrowed texts across large-scale archives of nineteenth-century periodicals. Cordell is also a Senior Fellow in the Andrew W. Mellon Society of Critical Bibliography at the Rare Book School and serves as the Delegate Assembly Representative for the MLA's Forum on Digital Humanities. 

Ken Fleischmann 

Kenneth R. Fleischmann is a professor in the School of Information at The University of Texas at Austin. He is also the Founding Chair of the Executive Team for Good Systems, a UT Grand Challenge (http://goodsystems.utexas.edu/) and the Founding Director of Undergraduate Studies for the iSchool's B.S.I./B.A. in Informatics. His research and teaching focus on the role of human values in the design and use of information technologies, specifically in the context of the ethics of AI. His research has been funded by the National Science Foundation (NSF), the Intelligence Advanced Research Projects Activity (IARPA), Microsoft Research, Microsoft Accessibility, Cisco Research, Micron Foundation, City of Austin, and the Public Interest Technology University Network. His research has been recognized by the iConference Best Paper Award, the iConference Best Short Research Paper Award, the ASIS&T SIG-USE Best Information Behavior Conference Paper Award, the ASIS&T SIG-SI Social Informatics Best Paper Award, the ALA Library Instruction Round Table Top Twenty Articles, the Civic Futures Award for Designing for the 100%, and the MetroLab Innovation of the Month Award.

Amir Sadovnik

Amir Sadovnik is an assistant professor of electrical engineering and computer science at the University of Tennessee. He received his PhD from the School of Electrical and Computer Engineering at Cornell University. His research in the fields of computer vision and machine learning has been mostly driven by trying to better understand how deep neural networks learn. His current research is mostly centered on adversarial attacks and there implications for both the robustness and trustworthiness of state-of-the-art machine learning methods. In this area he has explored research in both the image understanding domain as well as the natural language processing domain. His most recent work tries to train more explainable neural networks which in turn should be more robust to adversarial attacks.