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Aylin Caliskan My first name is pronounced as Eye-leen Assistant Professor Information School University of Washington Nonresident Fellow in Governance at Brookings   aylin@uw.edu        @aylin_cim

My research interests lie in artificial intelligence (AI) ethics, AI bias, computer vision, natural language processing, and machine learning, with an emphasis on trustworthiness and responsibility in AI. I study the impact of machine intelligence on society, especially threats to privacy and equity. I investigate the reasoning behind biased AI representations and decisions by developing explainability methods that uncover and quantify human-like biases learned by machines. Building these transparency enhancing algorithms involves the use of machine learning, natural language processing, and computer vision to interpret AI's co-evolution with society and gain insights on artificial and natural intelligence.

News

I am moving to the University of Washington at the end of summer 2021. My paper on AI bias is published in Science. Semantics derived automatically from language corpora contain human-like biases. source code I am the moderator of Computer Science - Computers and Society on arXiv.

Research 2021

Robert Wolfe and Aylin Caliskan Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models Empirical Methods in Natural Language Processing (EMNLP 2021) Autumn Toney-Wails and Aylin Caliskan ValNorm Quantifies Semantics to Reveal Consistent Valence Biases Across Languages and Over Centuries Empirical Methods in Natural Language Processing (EMNLP 2021) Aylin Caliskan Detecting and mitigating bias in natural language processing Brookings 2021 Akshat Pandey and Aylin Caliskan Disparate Impact of Artificial Intelligence Bias in Ridehailing Economy's Price Discrimination Algorithms AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AAAI/ACM AIES 2021) Wei Guo and Aylin Caliskan Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AAAI/ACM AIES 2021) Ryan Steed and Aylin Caliskan Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases The 2021 ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT 2021) Ryan Steed and Aylin Caliskan A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the Wild AI and Ethics, 2021 Autumn Toney, Akshat Pandey, Wei Guo, David Broniatowski, and Aylin Caliskan Automatically Characterizing Targeted Information Operations Through Biases Present in Discourse on Twitter 15th IEEE International Conference on Semantic Computing (ICSC 2021)