Xie, Siqi, Ning Luo, and Masaru Yarime, “Data Governance for Smart Cities in China: The Case of Shenzhen,” Policy Design and Practice (2023). This paper explores the appropriate system for governing various data in developing smart cities by investigating China’s data governance mechanisms and its distinctive institutional characteristics. Through conducting an exploratory study of the case of smart city development in Shenzhen and examining critical opportunities and challenges in data governance. Open data platforms have been developed through close cooperation between government and technology enterprises. Regulations have been introduced to protect data security and privacy and facilitate the exchange and use of data for innovation. However, it was found that stakeholders are not sufficiently incentivized to provide accurate information, resulting in the value of data not appropriately recognized or measured, discouraging the sharing and use of data. Papyshev, Gleb, and Masaru Yarime, “The Challenges of Industry Self-Regulation of AI in Emerging Economies: Implications of the Case of Russia for Public Policy and Institutional Development,” in Mark Findlay, Ong Li Min and Zhang Wenxi, eds., Elgar Companion to Regulating AI and Big Data in Emerging Economies, Edward Elgar, 81-98 (2023). This paper discusses how self-regulatory approaches popular for the governance of AI can potentially be problematic for emerging economies. The findings are derived from the fieldwork conducted in Russia in 2021-2022. The key challenges include the need for more technical expertise within the government, the lack of civil liberties, the interwovenness between the public and the private sector, the lack of motivation for ethical development, and protectionism over the local IT industry. Some initial remedies for the shortcomings of the industry self-regulation for AI in emerging economies can be found in how governments mitigate the negative effects of regulatory capture. These include promoting greater balance and diversity in the competition among different stakeholders, reforming the institutional context within which regulators operate, and opening up the regulatory process to various external checks and balances. Yarime, Masaru, ed., “Data and Sustainability,” Special Collection of Articles, Data & Policy, Cambridge University Press (2023). The articles in this special collection on Data Governance for Innovation for Sustainable Smart Cities and Facilitating Data-Driven Innovation for Sustainability explore policy measures and approaches to promote data-driven innovation for sustainable smart cities. Collaboration among stakeholders is crucial for collecting, sharing, and using various available data to foster innovation. However, differing interests and motivations among stakeholders may hinder data exchange. Concerns include handling sensitive data, data security, privacy, and ethical use for behavioral change. Policy challenges encompass data ownership, accessibility, interoperability, incentives for data sharing, security, privacy, public trust, and cross-border data transfer. Innovative policy approaches like living laboratories and regulatory sandboxes are being considered. It’s vital to assess the impacts of these policy measures on driving data-driven innovation while addressing societal concerns. 11 RESEARCH SHOWCASE Science Technology Innovation Policy
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