AI Regulation and Governance
In recent years, China has emerged as a pioneer in formulating some of the world’s earliest and most comprehensive rules concerning algorithms, deepfakes, and generative artificial intelligence (AI) services. This proactive intervention has left the impression that China has stood at the forefront as a global leader in regulating AI. Yet this perception put too much emphasis on the law on paper while overlooking the country’s intricate institutional dynamics. The Chinese government simultaneously acts as a policymaker, an investor, a supplier, a customer and a regulator in the AI sector. Given its extensive and deep involvement in the AI ecosystem, the government lacks a strong commitment to regulate the industry. Factors such as the intense US-China tech rivalry and the escalating chip embargo on Chinese AI firms further diminish the government’s incentive to impose strict regulation. Meanwhile, the current downturn in the Chinese economy and low market confidence impose further constraints on the government’s actions. Consequently, despite maintaining strict information control over public-facing AI services, China’s overall approach to AI regulation has been markedly business-friendly. Recent legislative measures, such as the interim measures to regulate generative AI and several local AI legislations, offer little protective value to the Chinese public. Instead, these laws have primarily served as an enabler by sending a strong pro-growth signal to the industry while attempting to coordinate various stakeholders to accelerate technological progress. As evidenced by its permissive stance over the abusive use of facial recognition technology, Chinese regulators have favoured a light-touch approach to AI regulation in practice. Similarly, Chinese courts are trying to prop up the AI industry, as demonstrated by the Beijing Internet Court’s decision to grant copyrights in an AI-generated image. China’s strategic lenient approach to regulation may therefore offer its AI firms a short-term competitive advantage over their European and U.S. counterparts. However, this leniency risks creating potential regulatory lags that could escalate into AI-induced accidents and even disasters. The dynamic complexity of China’s regulatory tactics therefore underscores the urgent need for increased international dialogue and collaboration with the country to tackle the safety challenges in AI governance.
The rapid advancement of generative AI is poised to disrupt the creative industry. Amidst the immense excitement for this new technology, its future development and applications in the creative industry hinge crucially upon two copyright issues: 1) the compensation to creators whose content has been used to train generative AI models (the fair use standard); and 2) the eligibility of AI-generated content for copyright protection (AI-copyrightability). While both issues have ignited heated debates among academics and practitioners, most analysis has focused on their challenges posed to existing copyright doctrines. In this paper, we aim to better understand the economic implications of these two regulatory issues and their interactions. By constructing a dynamic model with endogenous content creation and AI model development, we unravel the impacts of the fair use standard and AI-copyrightability on AI development, AI company profit, creators income, and consumer welfare, and how these impacts are influenced by various economic and operational factors. For example, while generous fair use (no compensation to creators for data used for AI training) benefits all parties when abundant training data exists, it could hurt creators and consumers when such data is scarce. Similarly, stronger AI-copyrightability (AI content enjoys more copyright protection) could hinder AI development, and reduce social welfare. Our analysis also highlights the complex interplay between these two copyright issues. For instance, when existing training data is scarce, generous fair use may be preferred only when AI-copyrightability is weak. Our findings underscore the need for policymakers to embrace a dynamic, context-specific approach in making regulatory decisions and provide insights for business leaders navigating the complexities of global regulatory environment.
• Platform Governance
Crowd-judging on Two-Sided Platforms: An Analysis of In-group Bias (with Alan Kwan & S. Alex Yang), MANAGEMENT SCIENCE (2023) [Management Science is a top business journal with a five-year impact factor of 7.7] (featured in the WSJ, First Prize of the Best Paper Award in the 2023 CSAMSE Annual Conference)
Disputes over transactions on two-sided platforms are common and are usually arbitrated through platforms’ customer service departments or third-party service providers. In this paper, we study crowd-judging, a novel crowd-sourcing mechanism whereby users (buyers and sellers) volunteer as jurors to decide disputes arising from the platform. To understand this phenomenon, we use a rich dataset from the dispute resolution center at Taobao, a leading Chinese e-commerce platform. While this mechanism enhances resolution speed, there are concerns that crowd-jurors may exhibit a form of in-group bias (where buyers favor the buyer and sellers favor the seller in a dispute), and that such in-group bias may systematically sway case outcomes given the majority of users on such platforms are buyers. We find evidence consistent with this concern: on average, a seller juror is approximately 10% likelier to vote for a seller. Such bias is 70% higher among cases that are less clear-cut and decided by a thin margin. Conversely, the bias reduces dramatically as users gain crowd-judging experience: in-group bias when jurors have the sample-median level of experience is 95% lower than when jurors are completely inexperienced. This suggests learning-by-doing may mitigate biases associated with socioeconomic identification. Partly due to this learning effect, our simulation shows that in-group bias influences the outcomes of no more than 2% of cases under the current randomized case allocation process, and can be further reduced under dynamic policies that better allocate experienced jurors. Such findings offer promising evidence that crowd-sourcing can be an effective dispute resolution mechanism to govern online platforms, and that properly designed operating policies can further improve its efficacy.
Improving Dispute Resolution in Two-Sided Platforms: The Case of Review Blackmail (with Yiangos Papanastasiou & S. Alex Yang), 69 MANAGEMENT SCIENCE 6021 (2023)
We study the relative merits of different dispute resolution mechanisms in two-sided platforms, in the context of disputes involving malicious reviews and blackmail. We develop a game-theoretic model of the strategic interactions between a seller and a (potentially malicious) consumer. In our model, the seller takes into account the impact of consumer reviews on his future earnings; recognizing this, a malicious consumer may attempt to blackmail the seller by purchasing the product, posting a negative review, and demanding ransom to remove it. Without a dispute resolution mechanism in place, the presence of malicious consumers in the market can lead to a significant decrease in seller profit, especially in settings characterized by high uncertainty about product quality. The introduction of a standard centralized dispute resolution mechanism (whereby the seller can report allegedly malicious reviews to the host platform, which then judges whether to remove the review) can restore efficiency to some extent, but requires the platform's judgments to be both very quick and highly accurate. We demonstrate that a more decentralized mechanism (whereby the firm is allowed to remove reviews without consulting the platform, subject to ex post penalties for wrongdoing) can be much more effective, while simultaneously alleviating -- almost entirely -- the need for the platform's judgments to be quick. Our results suggest that decentralization, when implemented correctly, may represent a more efficient approach to dispute resolution.
Agility Over Stability: China’s Great Reversal in Regulating the Platform Economy, 63 HARVARD INTERNATIONAL LAW JOURNAL 457 (2022)
This paper develops a new theoretical framework to analyze Chinese regulatory governance by considering the strategic interaction between four key players involved in the regulatory process: the top leadership, the regulators, the firms and the public. By focusing on China’s great reversal in regulating the platform economy, I argue that China’s volatile style of policymaking is deeply ingrained in its authoritarian governance, where power is centralized in the top leadership who also suffers from a chronic information deficit. This often leads to a policy control mechanism that fluctuates between very lax and very harsh enforcement. More specifically, I show how government support, firm lobbying and bureaucratic inertia together contributed to a lag in regulating online platforms. When a crisis loomed, the top leadership quickly mobilized all administrative resources and propaganda to initiate a law enforcement campaign against tech giants. However, without strong judicial oversight, aggressive agency interventions create the risk of over-enforcement and administrative abuse. Thus far, China’s reorientation of its policy control has significantly bolstered its regulatory capacity across various fronts including financial, antitrust and data regulation. By exerting greater oversight over platform governance, the government has pressured tech firms to transfer their wealth to the public to combat income inequality. The government’s heavy-handed approach has also afforded it great leverage to nudge tech firms to prioritize on cutting-edged technologies, and to steer them away from foreign stock markets, thus reducing reliance on the West for both technologies and capital. Despite the campaign’s immediate impact, it remains to be seen whether it will bring about lasting changes, especially in light of the persistent lobbying from tech firms and the risk of regulatory capture.
• Trade & Investment
US-China Trade Negotiation: A Contract Theory Perspective, 51 GEORGETOWN INTERNATIONAL LAW JOURNAL 809 (2020)
International trade negotiations have traditionally been viewed as a two-level political bargain between trading nations and among domestic interest groups. While this bargaining model is helpful for predicting the political dynamics in trade negotiations, its focus on politics tends to obscure the economic consequences of trade agreements. Drawing upon insights from contract theory in economics, this Article analyzes three ingredients of transaction costs that lead to the incompleteness of a trade agreement—the unforeseen contingencies, the cost of enforcing the contract, and the cost of writing the agreement. Using the Sino-U.S. trade negotiation between 2018 to 2019 as a comprehensive case study, this Article illustrates the circumstances when a trade agreement is difficult to write, unlikely to succeed, and impossible to enforce. As an alternative to a trade agreement, this Article advocates instead for greater economic integration as a commitment device. By allowing each country to hold the other’s assets hostage, economic integration can facilitate cooperation between nations when trust is lacking. This Article contributes to the existing literature by proposing an economic framework to analyze the promise and perils of trade negotiations. It also offers a cautionary tale of using economic sanction to force other countries to make legal concessions.
Inspired by psychological studies on human judgment, this Article represents the first attempt to provide a systematic account of how various heuristics and cognitive biases can influence public perception as well as regulatory response to foreign direct investment. In particular, it catalogues the main social and cognitive mechanisms through which various well-organized interest groups can exploit public fear of foreign direct investment from China. By closely studying two examples—the U.S. Congress’ hostile response to CNOOC’s attempted acquisition of Unocal and the European Commission’s increased antitrust scrutiny of Chinese state-owned enterprises’ acquisitions in Europe—this Article shows how undue fear of Chinese investment can lead to counterproductive regulatory response. Contrary to the popular perception that Chinese state-owned enterprises are mere puppets of the government, this Article draws attention to the pervasive but neglected agency problems that have powered the surge of Chinese outward investment. It calls for more effortful thinking by Western policymakers and cautions against extreme precautionary measures for investment from China. At the same time, however, it questions the wisdom of overseas investment by Chinese state-owned enterprises. Empire building incentives, exacerbated by weak corporate governance structures and the lack of financial disclosure, make it highly likely that state assets are squandered in overseas acquisitions.
• Chinese Political Economy
The latest debate about Chinese state owned enterprises (SOEs) revolves around whether there is a positive association between ownership and control, or whether all firms in China are similarly captured by the government. The recent Chinese Communist Party (Party)’s policy mandating all SOEs to amend their corporate charters to enhance the Party’s control has provided us with a rare opportunity to empirically investigate this question. We find that the state’s equity interest is positively correlated with an SOE’s responsiveness to the Party’s mandate, while the concentration level of nonstate owners and overseas listing are inversely related. These results show that ownership is important for the Party to exercise control over SOEs, but the Party also faces external constraints from other nonstate owners and overseas regulators and investors.