The societal and ethical implications of artificial intelligence (AI) have sparked discussions among academics, policymakers and the public around the world. What has gone unnoticed so far are the likewise vibrant discussions in China. We analyzed a large sample of discussions about AI ethics on two Chinese social media platforms. Findings suggest that participants were diverse, and included scholars, IT industry actors, journalists, and members of the general public. They addressed a broad range of concerns associated with the application of AI in various fields. Some even gave recommendations on how to tackle these issues. We argue that these discussions are a valuable source for understanding the future trajectory of AI development in China as well as implications for global dialogue on AI governance.

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With the emergence of cyber-physical systems (CPS), we are now at the brink of next computing revolution. IoT (Internet of Things) based Smart Grid (SG) is one of the foundations of this CPS revolution and defined as a power grid integrated with a large network of smart objects. The volume of time series of SG equipment is tremendous and the raw time series are very likely to contain missing values because of undependable network transferring. The problem of storing a tremendous volume of raw time series thereby providing a solid support for precise time series analytics now becomes tricky. In this paper, we propose a dependable time series analytics (DTSA) framework for IoT-based SG. Our proposed DTSA framework is capable of proving a dependable data transforming from CPS to the target database with an extraction engine to preliminary refining raw data and further cleansing the data with a correction engine built on top of a sensor-networkregularization based matrix factorization (SnrMF) method. The experimental results reveal that our proposed DTSA framework is capable of effectively increasing the dependability of raw time series transforming between CPS and the target database system through the online lightweight extraction engine and the offline correction engine. Our proposed DTSA framework would be useful for other industrial big data practices.

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