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Article Title: Bayesian modeling of human–AI complementarity Authors & Year: M. Steyvers, H. Tejeda, G. Kerrigan, and P. Smyth (2022) Journal:   Proceedings of the National Academy of Sciences of the United States of America [DOI:10.1073/pnas.2111547119] Review Prepared by David Han Exploration of Human-Machine Complementarity with CNN In recent years, artificial intelligence (AI) and machine learning (ML), especially deep learning, have advanced significantly for tasks like computer vision and speech recognition. Despite their high accuracy, these systems can still have weaknesses, especially in tasks like image and text classification. This has led to interest in hybrid systems where AI and humans collaborate, focusing on a more human-centered approach to AI design. Studies show humans and machines have complementary strengths, prompting the development of frameworks and platforms for their collaboration. To explore this further, the authors of the paper developed a Bayesian model for image classification tasks, analyzing predictions from both humans…

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