Shaping the Governance Framework towards the Artificial Intelligence from the Responsible Research and Innovation Given the wide spread use of robots and artificial intelligence (AI) across various industries, from autodriving, healthcare, military, mass media, service sector, large sets of information searching(legal records), financial services, and new business model, etc., both the developed countries and many emerging market economics attach highly importance to the development of AI. They successively make the toplevel design in the national strategy level, including China. However, the rapid development and the application of AI presents some alarming governance challenges, while to establish a solid governance system of AI is lagging behind its pace of technological development. Thereafter, conversations and debates on AI are organized, to hearing the different voices from scientists, technologies, ethics, social scientist, philosophers and the general public (the users), then to build up a practical governance system for the assessment of ethical risks, as well as to avoid the uncontrolled growth of the application of AI. In this study, we firstly summarize the ethics risks within society done by AI. Secondly, we discuss the elements of how to build up the governance system of AI, following by comparing the initiatives of AI in different countries or major AI institutions, and exploring the recent Chinese practices towards AI. In the final part, we propose to establish a generic ethical governance framework towards AI under the theory of the responsible research and innovation (RRI), thus, the open scientific governance system will be formed.

Beyond Near and LongTerm: Towards a Clearer Account of Research Priorities in AI Ethics and Society. One way of carving up the broad AI ethics and society research space that has emerged in recent years is to distinguish between nearterm and longterm research. While such ways of breaking down the research space can be useful, we put forward several concerns about the nearlongterm distinction gaining too much prominence in how research questions and priorities are framed. We highlight some ambiguities and inconsistencies in how the distinction is used, and argue that while there are differing priorities within this broad research community, these differences are not wellcaptured by the nearlongterm distinction. We unpack the nearlongterm distinction into four different dimensions, and propose some ways that researchers can communicate more clearly about their work and priorities using these dimensions. We suggest that moving towards a more nuanced conversation about research priorities can help establish new opportunities for collaboration, aid the development of more consistent and coherent research agendas, and enable identification of previously neglected research areas.