Developing machine learning methods theoretically grounded in implicit social cognition reveals that unsupervised machine learning captures associations, including human-like biases, objective facts, and historical information, from the hidden patterns in datasets. Machines that learn representations of language from corpora embed biases reflected in the statistical regularities of language. Similarly, image representations in computer vision contain biases due to stereotypical portrayals in vision datasets. On the one hand, principled methodologies for measuring associations in artificial intelligence provide a systematic approach to study society, language, vision, and learning. On the other hand, these methods reveal the potentially harmful biases in artificial intelligence applications built on general-purpose representations. As algorithms are accelerating consequential decision-making processes ranging from employment and university admissions to law enforcement and content moderation, open problems remain regarding the propagation and mitigation of biases in the expanding machine learning pipeline.
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 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. Her work on machine learning’s impact on fairness and privacy 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. In 2021, Caliskan was named a Nonresident Fellow at the Brookings Institution.