Rate My Professor Aylin Caliskan

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.