Rate My Professor Aylin Caliskan

Aylin Caliskan is an Associate Professor in the Information School at the University of Washington, with an adjunct appointment in the Paul G. Allen School of Computer Science and Engineering. She co-directs the UW Tech Policy Lab and holds a Nonresident Senior Fellow position in the Center for Technology Innovation at the Brookings Institution. Prior to her current roles, Caliskan served as an Assistant Professor of Computer Science at George Washington University and as a Postdoctoral Researcher and Fellow at Princeton University's Center for Information Technology Policy. Her academic background includes a Ph.D. in Computer Science from Drexel University's College of Computing and Informatics in 2015, an M.S. in Computer Science from Drexel University in 2013, an M.S. in Robotics from the University of Pennsylvania in 2011, a B.S. in Information Systems from SUNY-Binghamton University, and a B.S. in Information Systems Engineering from Istanbul Technical University.

Caliskan's research centers on artificial intelligence ethics, particularly examining biases in natural language processing, multimodal machine learning, and human-AI collaboration. She develops methods to detect, quantify, and mitigate biases, errors, and harms in AI systems. Her groundbreaking publication, 'Semantics derived automatically from language corpora contain human-like biases' (Science, 2017), demonstrated that machine learning models encode societal biases from training data, garnering over 4,800 citations. Other notable works include 'Detecting emergent intersectional biases: Contextualized word embeddings contain a distribution of human-like biases' (AAAI/ACM AIES, 2021), 'Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval' (AAAI/ACM AIES, 2024), 'Directionality and Representativeness are Differentiable Components of Stereotypes in Large Language Models' (PNAS Nexus, 2024), and 'Easily accessible text-to-image generation amplifies demographic stereotypes at large scale' (ACM FAccT, 2023). Caliskan has earned the NSF CAREER Award (2024) for research on generative AI's societal impacts, the Schmidt Sciences Award on AI and Advanced Computing (2025), recognition as one of the 100 Brilliant Women in AI Ethics (2023), IJCAI Early Career Spotlight (2023), Rising Star in EECS at Stanford University (2017), and multiple best paper awards, including the Andreas Pfitzmann Best Student Paper Award (PETS, 2012). Her influential research advances responsible AI development and informs policy on AI governance.