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Privacy is a key principle for developing ethical AI technologies, but how does including AI technologies in products and services change privacy risks? We constructed a taxonomy of AI privacy risks by an- alyzing 321 documented AI privacy incidents. We codifed how the unique capabilities and requirements of AI technologies described in those incidents generated new privacy risks, exacerbated known ones, or otherwise did not meaningfully alter the risk. We present 12 high-level privacy risks that AI technologies either newly created (e.g., exposure risks from deepfake pornography) or exacerbated (e.g., surveillance risks from collecting training data). One upshot of our work is that incorporating AI technologies into a product can alter the privacy risks it entails. Yet, current approaches to privacy-preserving AI/ML (e.g., federated learning, diferential pri- vacy, checklists) only address a subset of the privacy risks arising from the capabilities and data requirements of AI. 

Abstract Large nonlinear internal solitary waves (NLIWs) are known to transit west northwest across the northeastern South China Sea from generation sites around the two‐ridge system in the Luzon Strait. The waves are important because their energy flux and dissipation are several orders of magnitude larger than the surrounding ocean. The wave transit has been well studied up to about the 100 m isobath but observations in shallower water have been scarce. Using oceanographic moorings and an innovative distributed temperature sensing optical cable, the NLIW transformations were observed from 2000 to 2 m on the flanks of Dongsha Atoll (Pratas Reef). Possible outcomes included reflection, refraction around the island, wave breaking, and penetration into shallow water. Upslope penetration depended on incident wave amplitude and direction as well as the local stratification.