Asia – The Living Library

You Be the Judge: How Taobao Crowdsourced Its Courts

Curated on January 25, 2025January 29, 2025 by Stefaan Verhulst

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

Curated on January 20, 2025January 23, 2025 by Stefaan Verhulst

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

Curated on December 8, 2024December 8, 2024 by Stefaan Verhulst

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

Curated on October 30, 2024October 30, 2024 by Stefaan Verhulst

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

Curated on October 21, 2024October 24, 2024 by Stefaan Verhulst

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)”.

China: Autocracy 2.0

Curated on October 2, 2024October 2, 2024 by Stefaan Verhulst

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)”.

Need for Co-creating Urban Data Collaborative

Curated on September 29, 2024October 3, 2024 by Stefaan Verhulst

Blog by Gaurav Godhwani: “…The Government of India has initiated various urban reforms for our cities like — Atal Mission for Rejuvenation and Urban Transformation 2.0 (AMRUT 2.0), Smart Cities Mission (SCM), Swachh Bharat Mission 2.0 (SBM-Urban 2.0) and development of Urban & Industrial Corridors. To help empower cities with data, the Ministry of Housing & Urban Affairs(MoHUA) has also launched various data initiatives including — DataSmart Cities Strategy, Data Maturity Assessment Framework, Smart Cities Open Data Portal, City Innovation Exchange, India Urban Data Exchange and the India Urban Observatory.

Unfortunately, most of the urban data remains in silos and capacities for our cities to harness urban data to improve decision-making, strengthen citizen participation continues to be limited. As per the last Data Maturity Assessment Framework (DMAF) assessment conducted in November 2020 by MoHUA, among 100 smart cities only 45 cities have drafted/ approved their City Data Policies with just 32 cities having a dedicated data budget in 2020–21 for data-related activities. Moreover, in-terms of fostering data collaborations, only 12 cities formed data alliances to achieve tangible outcomes. We hope smart cities continue this practice by conducting a yearly self-assessment to progress in their journey to harness data for improving their urban planning.

Seeding Urban Data Collaborative to advance City-level Data Engagements

There is a need to bring together a diverse set of stakeholders including governments, civil societies, academia, businesses and startups, volunteer groups and more to share and exchange urban data in a secure, standardised and interoperable manner, deriving more value from re-using data for participatory urban development. Along with improving data sharing among these stakeholders, it is necessary to regularly convene, ideate and conduct capacity building sessions and institutionalise data practices.

Urban Data Collaborative can bring together such diverse stakeholders who could address some of these perennial challenges in the ecosystem while spurring innovation…(More)”

China’s Hinterland Becomes A Critical Datascape

Curated on September 28, 2024October 3, 2024 by Stefaan Verhulst

Article by Gary Zhexi Zhang: “In 2014, the southwestern province of Guizhou, a historically poor and mountainous area, beat out rival regions to become China’s first “Big Data Comprehensive Pilot Zone,” as part of a national directive to develop the region — which is otherwise best known as an exporter of tobacco, spirits and coal — into the infrastructural backbone of the country’s data industry. Since then, vast investment has poured into the province. Thousands of miles of highway and high-speed rail tunnel through the mountains. Driving through the province can feel vertiginous: Of the hundred highest bridges in the world, almost half are in Guizhou, and almost all were built in the last 15 years.

In 2015, Xi Jinping visited Gui’an New Area to inaugurate the province’s transformation into China’s “Big Data Valley,” exemplifying the central government’s goal to establish “high quality social and economic development,” ubiquitously advertised through socialist-style slogans plastered on highways and city streets…(More)”.

China’s biggest AI model is challenging American dominance

Curated on September 25, 2024September 25, 2024 by Stefaan Verhulst

Article by Sam Eifling: “So far, the AI boom has been dominated by U.S. companies like OpenAI, Google, and Meta. In recent months, though, a new name has been popping up on benchmarking lists: Alibaba’s Qwen. Over the past few months, variants of Qwen have been topping the leaderboards of sites that measure an AI model’s performance.

“Qwen 72B is the king, and Chinese models are dominating,” Hugging Face CEO Clem Delangue wrote in June, after a Qwen-based model first rose to the top of his company’s Open LLM leaderboard.

It’s a surprising turnaround for the Chinese AI industry, which many thought was doomed by semiconductor restrictions and limitations on computing power. Qwen’s success is showing that China can compete with the world’s best AI models — raising serious questions about how long U.S. companies will continue to dominate the field. And by focusing on capabilities like language support, Qwen is breaking new ground on what an AI model can do — and who it can be built for.

Those capabilities have come as a surprise to many developers, even those working on Qwen itself. AI developer David Ng used Qwen to build the model that topped the Open LLM leaderboard. He’s built models using Meta and Google’s technology also but says Alibaba’s gave him the best results. “For some reason, it works best on the Chinese models,” he told Rest of World. “I don’t know why.”..(More)”