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3. Improving Human-AI Collaboration With Descriptions of AI Behavior

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   Cabrera, Ángel_Alexander; Perer, Adam; Hong, Jason_I (April 2023, Proceedings of the ACM on Human-Computer Interaction)

   People work with AI systems to improve their decision making, but often under- or over-rely on AI predictions and perform worse than they would have unassisted. To help people appropriately rely on AI aids, we propose showing them behavior descriptions, details of how AI systems perform on subgroups of instances. We tested the efficacy of behavior descriptions through user studies with 225 participants in three distinct domains: fake review detection, satellite image classification, and bird classification. We found that behavior descriptions can increase human-AI accuracy through two mechanisms: helping people identify AI failures and increasing people's reliance on the AI when it is more accurate. These findings highlight the importance of people's mental models in human-AI collaboration and show that informing people of high-level AI behaviors can significantly improve AI-assisted decision making. 

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4. Interfacing Nanophotonics with Deep Neural Networks: AI for Photonic Design and Photonic Implementation of AI

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   Abstract Recent remarkable progress in artificial intelligence (AI) has garnered tremendous attention from researchers, industry leaders, and the general public, who are increasingly aware of AI's growing impact on everyday life. The advancements of AI and deep learning have also significantly influenced the field of nanophotonics. On the one hand, deep learning facilitates data‐driven strategies for optimizing and solving forward and inverse problems of nanophotonic devices. On the other hand, photonic devices offer promising optical platforms for implementing deep neural networks. This review explores both AI for photonic design and photonic implementation of AI. Various deep learning models and their roles in the design of photonic devices are introduced, analyzing the strengths and challenges of these data‐driven methodologies from the perspective of computational cost. Additionally, the potential of optical hardware accelerators for neural networks is discussed by presenting a variety of photonic devices capable of performing linear and nonlinear operations, essential building blocks of neural networks. It is believed that the bidirectional interactions between nanophotonics and AI will drive the coevolution of these two research fields. 

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