Can Real-Time Metrics Fill China’s Data Gap?


Case-study by Danielle Goldfarb: “After Chinese authorities abruptly reversed the country’s zero-COVID policy in 2022, global policymakers needed a clear and timely picture of the economic and health fallout.

China’s economy is the world’s second largest and the country has deep global links, so an accurate picture of its trajectory mattered for global health, growth and inflation. Getting a solid read was a challenge, however, since official health and economic data not only were not timely, but were widely viewed as unreliable.

There are now vast amounts and varied types of digital data available, from satellite images to social media text to online payments; these, along with advances in artificial intelligence (AI), make it possible to collect and analyze digital data in ways previously impossible.

Could these new tools help governments and global institutions refute or confirm China’s official picture and gather more timely intelligence?..(More)”.

China wants tech companies to monetize data, but few are buying in


Article by Lizzi C. Lee: “Chinese firms generate staggering amounts of data daily, from ride-hailing trips to online shopping transactions. A recent policy allowed Chinese companies to record data as assets on their balance sheets, the first such regulation in the world, paving the way for data to be traded in a marketplace and boost company valuations. 

But uptake has been slow. When China Unicom, one of the world’s largest mobile operators, reported its earnings recently, eagle-eyed accountants spotted that the company had listed 204 million yuan ($28 million) in data assets on its balance sheet. The state-owned operator was the first Chinese tech giant to take advantage of the Ministry of Finance’s new corporate data policy, which permits companies to classify data as inventory or intangible assets. 

“No other country is trying to do this on a national level. It could drive global standards of data management and accounting,” Ran Guo, an affiliated researcher at the Asia Society Policy Institute specializing in data governance in China, told Rest of World. 

In 2023 alone, China generated 32.85 zettabytes — more than 27% of the global total, according to a government survey. To put that in perspective, storing this volume on standard 1-terabyte hard drives would require more than 32 billion units….Tech companies that are data-rich are well-positioned tobenefit from logging data as assets to turn the formalized assets into tradable commodities, said Guo. But companies must first invest in secure storage and show that the data is legally obtained in order to meet strict government rules on data security. 

“This can be costly and complex,” he said. “Not all data qualifies as an asset, and companies must meet stringent requirements.” 

Even China Unicom, a state-owned enterprise, is likely complying with the new policy due to political pressure rather than economic incentive, said Guo, who conducted field research in China last year on the government push for data resource development. The telecom operator did not respond to a request for comment. 

Private technology companies in China, meanwhile, tend to be protective of their data. A Chinese government statement in 2022 pushed private enterprises to “open up their data.” But smaller firms could lack the resources to meet the stringent data storage and consumer protection standards, experts and Chinese tech company employees told Rest of World...(More)”.

The Missing Pieces in India’s AI Puzzle: Talent, Data, and R&D


Article by Anirudh Suri: “This paper explores the question of whether India specifically will be able to compete and lead in AI or whether it will remain relegated to a minor role in this global competition. The paper argues that if India is to meet its larger stated ambition of becoming a global leader in AI, it will need to fill significant gaps in at least three areas urgently: talent, data, and research. Putting these three missing pieces in place can help position India extremely well to compete in the global AI race.

India’s national AI mission (NAIM), also known as the IndiaAI Mission, was launched in 2024 and rightly notes that success in the AI race requires multiple pieces of the AI puzzle to be in place.3 Accordingly, it has laid out a plan across seven elements of the “AI stack”: computing/AI infrastructure, data, talent, research and development (R&D), capital, algorithms, and applications.4

However, the focus thus far has practically been on only two elements: ensuring the availability of AI-focused hardware/compute and, to some extent, building Indic language models. India has not paid enough attention to, acted toward, and put significant resources behind three other key enabling elements of AI competitiveness, namely data, talent, and R&D…(More)”.

You Be the Judge: How Taobao Crowdsourced Its Courts


Excerpt from Lizhi Liu’s new book, “From Click to Boom”: “When disputes occur, Taobao encourages buyers and sellers to negotiate with each other first. If the feuding parties cannot reach an agreement and do not want to go to court, they can use one of Taobao’s two judicial channels: asking a Taobao employee to adjudicate or using an online jury panel to arbitrate. This section discusses the second channel, a unique Chinese institutional innovation.

Alibaba’s Public Jury was established in 2012 to crowdsource justice. It uses a Western-style jury-voting mechanism to solve online disputes and controversial issues. These jurors are termed “public assessors” by Taobao. Interestingly, the name “public assessor” was drawn from the Chinese talent show “Super Girl” (similar to “American Idol”), which, after the authority shut down its mass voting system, transitioned to using a small panel of audience representatives (or “public assessors”) to vote for the show’s winner. The public jury was widely used by the main Taobao site by 2020 and is now frequently used by Xianyu, Taobao’s used-goods market.

Why did Taobao introduce the jury system? Certainly, as Taobao expanded, the volume of online disputes surged, posing challenges for the platform to handle all disputes by itself. However, according to a former platform employee responsible for designing this institution, the primary motivation was not the caseload. Instead, it was propelled by the complexity of online disputes that proved challenging for the platform to resolve alone. Consequently, they opted to involve users in adjudicating these cases to ensure a fairer process rather than solely relying on platform intervention.

To form a jury, Taobao randomly chooses each panel of 13 jurors from 4 million volunteer candidates; each juror may participate in up to 40 cases per day. The candidate needs to be an experienced Taobao user (i.e., those who have registered for more than a year) with a good online reputation (i.e., those who have a sufficiently high credit rating, as discussed below). This requirement is high enough to prevent most dishonest traders from manipulating votes, but low enough to be inclusive and keep the juror pool large. These jurors are unpaid yet motivated to participate. They gain experience points that can translate into different virtual titles or that can be donated to charity by Taobao as real money…(More)”

Local Government and Citizen Co-production Through Neighborhood Associations


Chapter by Kohei Suzuki: “This chapter explores co-production practices of local governments, focusing on the role of neighborhood associations (NHAs) in Japan. This chapter investigates the overall research question of this book: how to enhance government performance under resource constraints, by focusing on the concept of citizen co-production. Historically, NHAs have played a significant role in supplementing municipal service provisions as co-producers for local governments. Despite the rich history of NHAs and their contributions to public service delivery at the municipal level, theoretical and empirical studies on NHAs and co-production practices remain limited. This chapter aims to address this research gap by exploring the following research questions: What are NHAs from a perspective of citizen co-production? What are the potential contributions of studying NHAs to the broader theory of co-production? What are the future research agendas? The chapter provides an overview of the origin and evolution of the co-production concept. It then examines the main characteristics and activities of NHAs and discusses their roles in supplementing local public service provision. Finally, the chapter proposes potential research agendas to advance studies on co-production using Japan as a case study…(More)”.

Privacy guarantees for personal mobility data in humanitarian response


Paper by Nitin Kohli,  Emily Aiken & Joshua E. Blumenstock: “Personal mobility data from mobile phones and other sensors are increasingly used to inform policymaking during pandemics, natural disasters, and other humanitarian crises. However, even aggregated mobility traces can reveal private information about individual movements to potentially malicious actors. This paper develops and tests an approach for releasing private mobility data, which provides formal guarantees over the privacy of the underlying subjects. Specifically, we (1) introduce an algorithm for constructing differentially private mobility matrices and derive privacy and accuracy bounds on this algorithm; (2) use real-world data from mobile phone operators in Afghanistan and Rwanda to show how this algorithm can enable the use of private mobility data in two high-stakes policy decisions: pandemic response and the distribution of humanitarian aid; and (3) discuss practical decisions that need to be made when implementing this approach, such as how to optimally balance privacy and accuracy. Taken together, these results can help enable the responsible use of private mobility data in humanitarian response…(More)”.

South Korea leverages open government data for AI development


Article by Si Ying Thian: “In South Korea, open government data is powering artificial intelligence (AI) innovations in the private sector.

Take the case of TTCare which may be the world’s first mobile application to analyse eye and skin disease symptoms in pets.

AI Hub allows users to search by industry, data format and year (top row), with the data sets made available based on the particular search term “pet” (bottom half of the page). Image: AI Hub, provided by courtesy of Baek

The AI model was trained on about one million pieces of data – half of the data coming from the government-led AI Hub and the rest collected by the firm itself, according to the Korean newspaper Donga.

AI Hub is an integrated platform set up by the government to support the country’s AI infrastructure.

TTCare’s CEO Heo underlined the importance of government-led AI training data in improving the model’s ability to diagnose symptoms. The firm’s training data is currently accessible through AI Hub, and any Korean citizen can download or use it.

Pushing the boundaries of open data

Over the years, South Korea has consistently come up top in the world’s rankings for Open, Useful, and Re-usable data (OURdata) Index.

The government has been pushing the boundaries of what it can do with open data – beyond just making data usable by providing APIs. Application Programming Interfaces, or APIs, make it easier for users to tap on open government data to power their apps and services.

There is now rising interest from public sector agencies to tap on such data to train AI models, said South Korea’s National Information Society Agency (NIA)’s Principal Manager, Dongyub Baek, although this is still at an early stage.

Baek sits in NIA’s open data department, which handles policies, infrastructure such as the National Open Data Portal, as well as impact assessments of the government initiatives…(More)”

AI in the Public Service: Here for Good


Special Issue of Ethos: “…For the public good, we want AI to help unlock and drive transformative impact, in areas where there is significant potential for breakthroughs, such as cancer research, material sciences or climate change. But we also want to raise the level of generalised adoption. For the user base in the public sector, we want to learn how best to use this new tool in ways that can allow us to not only do things better, but do better things.

This is not to suggest that AI is always the best solution: it is one of many tools in the digital toolkit. Sometimes, simpler computational methods will suffice. That said, AI represents new, untapped potential for the Public Service to enhance our daily work and deliver better outcomes that ultimately benefit Singapore and Singaporeans….

To promote general adoption, we made available AI tools, such as Pair, 1 SmartCompose, 2 and AIBots. 3 They are useful to a wide range of public officers for many general tasks. Other common tools of this nature may include chatbots to support customer-facing and service delivery needs, translation, summarisation, and so on. Much of what public officers do involves words and language, which is an area that LLM-based AI technology can now help with.

Beyond improving the productivity of the Public Service, the real value lies in AI’s broader ability to transform our business and operating models to deliver greater impact. In driving adoption, we want to encourage public officers to experiment with different approaches to figure out where we can create new value by doing things differently, rather than just settle for incremental value from doing things the same old ways using new tools.

For example, we have seen how AI and automation have transformed language translation, software engineering, identity verification and border clearance. This is just the beginning and much more is possible in many other domains…(More)”.

Digital Distractions with Peer Influence: The Impact of Mobile App Usage on Academic and Labor Market Outcomes


Paper by Panle Jia Barwick, Siyu Chen, Chao Fu & Teng Li: “Concerns over the excessive use of mobile phones, especially among youths and young adults, are growing. Leveraging administrative student data from a Chinese university merged with mobile phone records, random roommate assignments, and a policy shock that affects peers’ peers, we present, to our knowledge, the first estimates of both behavioral spillover and contextual peer effects, and the first estimates of medium-term impacts of mobile app usage on academic achievement, physical health, and labor market outcomes. App usage is contagious: a one s.d. increase in roommates’ in-college app usage raises own app usage by 4.4% on average, with substantial heterogeneity across students. App usage is detrimental to both academic performance and labor market outcomes. A one s.d. increase in own app usage reduces GPAs by 36.2% of a within-cohort-major s.d. and lowers wages by 2.3%. Roommates’ app usage exerts both direct effects (e.g., noise and disruptions) and indirect effects (via behavioral spillovers) on GPA and wage, resulting in a total negative impact of over half the size of the own usage effect. Extending China’s minors’ game restriction policy of 3 hours per week to college students would boost their initial wages by 0.7%. Using high-frequency GPS data, we identify one underlying mechanism: high app usage crowds out time in study halls and increases absences from and late arrivals at lectures…(More)”.

China: Autocracy 2.0


Paper by David Y. Yang: “Autocracy 2.0, exemplified by modern China, is economically robust, technologically advanced, globally engaged, and controlled through subtle and sophisticated methods. What defines China’s political economy, and what drives Autocracy 2.0? What is its future direction? I start by discussing two key challenges autocracies face: incentives and information. I then describe Autocracy 1.0’s reliance on fear and repression to address these issues. It makes no credible promises, using coercion for compliance, resulting in a low-information environment. Next, I introduce Autocracy 2.0, highlighting its significant shift in handling commitment and information challenges. China uses economic incentives to align interests with regime survival, fostering support. It employs advanced bureaucratic structures and technology to manage incentives and information, enabling success in a high-information environment. Finally, I explore Autocracy 3.0’s potential. In China, forces might revert to Autocracy 1.0, using technology for state control as growth slows but aspirations stay high. Globally, modern autocracies, led by China, are becoming major geopolitical forces, challenging the liberal democratic order…(More)”.