Our Research

White Paper for Universities on Navigating Artificial Intelligence Innovation Ecosystems in the area of AI Governance

Published Date: 08/09/2025

Author(s): Raphaël Weuts, Robert Kerwin Billones, Johannes Bleher, Pawel PUJSZO, Rozanne Tuesday FLORES, Zsolt Almási, Xuanyang Guo, Jerrold Soh

The rapid transformation of industries and societies due to Artificial Intelligence (AI) calls for the establishment of robust AI guiding principles, governance frameworks, policies and standards to ensure its ethical, transparent, equitable and safe development. The role of higher education institutions (HEIs) as a pillar of knowledge and a moral compass in the age of AI becomes ever more important. This white paper identifies a set of foundational actions that HEIs must take, in varying forms depending on AI’s trajectory. It also highlights specific implementable strategies that can effectively contribute to global AI governance, including social, legal and technical mechanisms that guide safe and efficient development, deployment and use of AI: First, universities must strengthen their leadership in setting the directions of AI development and implementation, guiding the balance between innovation and its responsible use. They must also act as a guiding voice within society, addressing the challenges and implications associated with advanced AI risks. Finally, the role of universities in promoting ethical AI governance and fostering cross-border collaboration in AI policy is crucial, particularly in establishing mechanisms for mutual accountability, resource sharing, and cooperative oversight.

This work examines four potential scenarios of AI evolution, each with distinct governance challenges and opportunities: a new AI winter, a temporarily sustained current trajectory, AI asymptotically slowing towards the qualitative human-level, and AI surpassing that baseline. This paper outlines essential governance actions for university leaders, identifies key stakeholders, and proposes measurable steps. This white paper can serve as a resource for university leaders and decision-makers in navigating the complexities of responsible AI governance. 

Weuts, Raphaël and Weuts, Raphaël and Billones, Robert Kerwin and Bleher, Johannes and PUJSZO, Pawel and FLORES, Rozanne Tuesday and Almási, Zsolt and Guo, Xuanyang and Soh, Jerrold, White Paper for Universities on Navigating Artificial Intelligence Innovation Ecosystems in the area of AI Governance (March 05, 2025). Available at SSRN: https://ssrn.com/abstract=5419114 or http://dx.doi.org/10.2139/ssrn.5419114

Silicon Statecraft Alignment: Taiwan's Strategic Bet on US-Led Export Controls

Published Date: 17/07/2025

Author(s): Ching-Fu Lin and Han-Wei Liu

Taiwan’s export control regime is becoming more closely aligned with U.S. policy, re-calibrating its role in an emerging U.S.-anchored system of networked choke point statecraft. Since 2018, the United States – citing national security threats – has pursued aggressive measures to constrain China’s access to cutting-edge technologies. In August 2020, the U.S. Bureau of Industry and Security (BIS) expanded the Foreign Direct Product Rule under the Export Control Reform Act (ECRA)/Export Administration Regulations (EAR) regime, requiring Taiwanese firms and others at key supply chain choke points to comply with the tightened U.S. controls. The extraterritorial rules operate on the principle that any foreign-made chip becomes subject to U.S. laws if it is the direct product of U.S. technology, software, or equipment – extending U.S. export controls deep into global semiconductor supply chains. In practice, enforcement gaps have persisted, driven partly by the lack of coordination between the U.S. and other choke point economies amid supply chain complexity, insufficient law enforcement and compliance, shifting technologies, and competing interests even within the network of “like-minded countries.”

Lin, Ching-Fu and Liu, Han-Wei, Silicon Statecraft Alignment: Taiwan's Strategic Bet on US-Led Export Controls (June 25, 2025). The Diplomat; ISSN: 1446697X; 2025-07-03, Available at SSRN: https://ssrn.com/abstract=5341600

The Case for a Taiwan-US Semiconductor Agreement

Published Date: 27/06/2025

Author(s): Ching-Fu Lin, Han-Wei Liu

[...]moves transcend their face value, as they raise fundamental challenges to local regulatory frameworks concerning foreign investment reviews, core key technology protection, and, particularly in the case of Taiwan, national security law. The U.S. global market share fell from 70 percent to 20 percent, while Japan’s surged from 30 percent to 75 percent (though interestingly, Japan’s share of the U.S. market remained insignificant during this period). The framework expanded U.S. market access while curbing Japanese firms’ dominance domestically and abroad, benefiting U.S. chipmakers. To some extent, the arrangement may make long-term business sense for the TSMC as it considers operations diversification, energy and water resilience, workforce shortages, and overall production capacity.

Lin, Ching-Fu and Liu, Han-Wei, The Case for a Taiwan-US Semiconductor Agreement
(March 12, 2025). The Diplomat, Available at SSRN: https://ssrn.com/abstract=

Techno-Geopolitics and Semi-conductor Chokepoints: Beyond the US-China WTO Dispute

Published Date: 24/06/2025

Author(s): Han-Wei Liu, Ching-Fu Lin

Much ink has been spilled on the US-China tech and trade war, particularly the role of US export controls in containing China and preserving America's tech dominance. Less attention, however, has been paid to other critical nodes in the global semiconductor supply chain-Taiwan, South Korea, Japan, and the Netherlands-which we coin as 'chokepoint economies'. Taiwan alone produces over 90% of the world's most advanced chips, vital to the digital age and the future of AI in the Indo-Pacific and beyond. Using Taiwan as a case study, this paper shows how chokepoint economies navigate competing pressures amid intensifying great power rivalry. We enrich the scholarly and policy debates by making three interrelated claims. First, while the US has relied heavily on extraterritorial export controls to limit China's access to advanced chips, chokepoint economies like Taiwan may adopt more permissive policies. This potential divergence reflects their distinct roles and agendas in the supply chain and strategic balancing between economic and geopolitical interests. Such regulatory gaps, in turn, raise important questions about where legal responsibility lies and how effective US unilateralism can be over time. For one, if China's restricted access results primarily from US laws rather than chokepoint economies, any legal challenge would target the US-which has substantially disengaged from the WTO dispute

Liu, Han-Wei and Lin, Ching-Fu, Techno-Geopolitics and Semi-conductor Chokepoints: Beyond the US-China WTO Dispute (January 16, 2025). Available at SSRN: https://ssrn.com/abstract=5315364 or http://dx.doi.org/10.2139/ssrn.5315364

Understanding Disputes Over Digitalization: A Perspective of Cross-Border Central Bank Digital Currencies

Published Date: 27/05/2025

Author(s): Heng Wang

In a rapidly digitalizing world, complex disputes would arise due to the technological, regulatory and other complexities. Public and private actorsincluding central banks, financial institutions, tech firms, and law firmsneed to understand the complexity of these disputes before determining whether and how to engage with digitalization. Given the fundamental role of currency in the world economy, this article examines central bank digital currency (CBDC) as a case study. Representing the digitalization of central bank money, CBDCs are an unprecedented digital form of national currency issued by central banks. Various central banks, including those of the Eurozone, the United Kingdom, and China, are actively exploring CBDCs. Many states, such as China, are working on the possible crossborder use of CBDCs. There have been over ten cross-border CBDC projects, and more crossborder CBDC projects are planned. These cross-border CBDCs represent a transformative shift given new infrastructure, practices, and issues. As a result, a nascent CBDC network is emerging. However, the complexity of disputes over digitalization remains a critically understudied issue. Drawing from the case of CBDCs, this article addresses a key question: how to understand the complexity of disputes over digitalization? This study applies a structured framework to analyzing digital disputes: social (stakeholder interests and interactions), material (subject matter and party perceptions), and temporal (timing and evolution of disputes). By critically analyzing these dimensions, this article provides a forward-looking approach to understanding disputes over digitalization. It contributes to both theory and practice by: (i) offering a critical analysis of the practical constraints actors face in addressing disputes arising from digitalization, and (ii) bridging the silos of dispute settlement and digital transformation, fostering a more integrated approach to governance and regulation.

Wang, Heng, Understanding Disputes Over Digitalization: A Perspective of Cross-Border Central Bank Digital Currencies (June 21, 2025). Available at SSRN: https://ssrn.com/abstract=5251047 or http://dx.doi.org/10.2139/ssrn.5251047

Artificial Intelligence in the Regulatory Wonderland

Published Date: 06/05/2025

Author(s): Jerrold Soh, Larissa Lim, Zee Kin Yeong

This Chapter conducts a theoretical and practical examination of existing AI governance and regulatory models. With “regulation” we include not only formal legal rules but also soft laws, industry codes, and other regulatory modes. Using existing approaches as case studies, we comment on potential advantages and disadvantages of each model, as well as seek to extract common themes and challenges that accompany AI governance and regulation more generally. Part II sets the theoretical context by scrutinizing two key dimensions implied by the term “AI regulation” itself: (1) “AI” as an object of regulation; and (2) “regulation” itself as a subject of discussion. In Part III, which forms the bulk of this Chapter’s contribution, we visit selected checkpoints along the regulatory spectrum, specifically including self-, co-, quasi-, and direct regulation. Each model will be defined using the regulatory theory literature, exemplified via real world legal instruments, and evaluated by applying the former to the latter. Part IV synthesizes general themes and insights emerging from Part III’s regulatory tour. It identifies how regulatory assessments of the risks and benefits of AI systems appear to differ substantively across jurisdictions. Given the challenges inherent in regulating the use of an opaque, complex, and relatively nascent technology, Part IV further identifies the potential utility of a rigorous AI testing framework. 

Soh, Jerrold and Lim, Larissa and Lim, Larissa and Yeong, Zee Kin, Artificial Intelligence in the Regulatory Wonderland (July 29, 2024). Singapore Management University School of Law Research Paper Forthcoming, Available at SSRN: https://ssrn.com/abstract=5226111 or http://dx.doi.org/10.2139/ssrn.5226111

The Practice of Law

Published Date: 01/05/2025

Author(s): Jerrold Tsin Howe SOH, Rajesh SREENIVASAN, Dorcas QUEK ANDERSON, Eunice CHUA, How Khang LIM

SOH, Jerrold Tsin Howe; SREENIVASAN, Rajesh; Dorcas QUEK ANDERSON; CHUA, Eunice; and LIM, How Khang. The practice of law. (2025). Law and technology in Singapore.

Governing Intelligence: Singapore’s Evolving AI Governance Framework

Published Date: 15/01/2025

Author(s): Jason G Allen, Jane Loo, Jose Luna

This paper provides an outline analysis of the evolving governance framework for Artificial Intelligence (AI) in Singapore. Across the Singapore government, AI solutions are being adopted in line with Singapore’s “Smart Nation Initiative” to leverage technology to make impactful changes to the nation and the economy. In tandem, Singaporean authorities have been assiduous to release a growing number of governance documents, which we analyse together to chart the city-state’s approach to AI governance in international comparison. Characteristics of Singapore’s AI governance approach include an emphasis on consensus- building between stakeholders (particularly government and industry but also citizens) and voluntary or “quasi” regulation, lately with an emphasis on promulgating standards (AI Standards, n.d.) and audit-like frameworks. Singaporean regulators have also been early movers (globally, and especially in the region) in the promulgation of normative instruments on AI governance including developing the world’s first AI Governance Testing Framework and Toolkit, AI Verify. The Singapore approach may be compelling for other jurisdictions in the region and around the world with an interest in a collaborative, balanced, and consensual approach to governing AI outside of strict regulatory mechanisms. However, any jurisdiction adopting aspects of its evolving model would have to duly account for relevant diOerences in social and institutional conditions.

Allen, Jason G and Loo, Jane and Luna, Jose, Governing Intelligence: Singapore’s Evolving AI Governance Framework (October 01, 2024). SMU Centre for Digital Law Research Paper No. 4/2025, Available at SSRN: https://ssrn.com/abstract=5097834 or http://dx.doi.org/10.2139/ssrn.5097834

Mapping Generative AI Regulation in Finance and Bridging Regulatory Gaps

Published Date: 14/11/2024

Remolina, Nydia, Mapping Generative AI Regulation in Finance and Bridging Regulatory Gaps (September 30, 2024). Journal of Financial Transformation, Forthcoming, Singapore Management University School of Law Research Paper 38/2024, Available at SSRN: https://ssrn.com/abstract=4973307 or http://dx.doi.org/10.2139/ssrn.4973307 

Generative AI in Finance: Risks and Potential Solutions

Published Date: 09/11/2024

Remolina, Nydia, Generative AI in Finance: Risks and Potential Solutions (November 9, 2023). Singapore Management University School of Law Research Paper Forthcoming, SMU Centre for AI & Data Governance Research Paper Forthcoming, Available at SSRN: https://ssrn.com/abstract=4628235 or http://dx.doi.org/10.2139/ssrn.4628235 

Can federated learning solve AI’s data privacy problem?: A legal analysis

Published Date: 01/10/2024

Author(s): Warren B. CHIK, Florian GAMPER

Federated learning (FL) is a method of training AI systems on different datasets without sharing data. The promise of FL is to enable AI systems to be trained on data, including personal data, while preserving data privacy and confidentiality, and thus, inter alia, facilitate compliance with data protection legislation. FL has generated a considerable interest amongst the computer science community, yet there is a dearth of legal analysis of FL. This is a problem because the question of whether FL facilitates compliance with data protection legislation is a legal question. This article will fill this lacuna by providing a comprehensive legal analysis of FL through an examination of how the EU’s General Data Protection Regulation (GDPR) applies to FL. This article postulates that, from a legal perspective, FL can be an effective method of facilitating compliance with data protection regulations. However, this article expresses doubt that, without support from policy makers and regulators, FL will be used sufficiently widely to make significantly more data available for the training of AI systems, than is currently the case.

CHIK, Warren B. and GAMPER, Florian. Can federated learning solve AI’s data privacy problem?: A legal analysis. (2024). Rutgers Law Journal. 52, 1-40.
Available at: https://ink.library.smu.edu.sg/sol_research/4517

From CPTPP to U.S.-Taiwan Initiative on 21st-Century Trade: The Evolution of Good Regulatory Practices and its Implications for Taiwan

Published Date: 24/09/2024

Author(s): Han-Wei Liu

This paper examines the evolution and implications of Good Regulatory Practices (GRP)otherwise known as "regulatory coherence" in international trade agreements, focusing on the US-Taiwan Initiative on 21st-Century Trade. By comparing GRP frameworks in the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP), the United States-Mexico-Canada Agreement (USMCA), and the US-Taiwan Initiative, the paper highlights the challenges of integrating GRP into Taiwan's legal system. The analysis reveals significant disparities between GRP requirements and Taiwan's existing Administrative Procedure Act in key areas such as public consultation, regulatory impact assessments, and sunset review mechanisms. The paper also explores potential ramifications for Taiwan's export control regime, particularly for dual-use technologies like semiconductors, revealing tensions between GRP's transparency and public consultation demands and confidentiality needs. While GRP aims to enhance regulatory quality and facilitate trade, its implementation in Taiwan necessitates complex legislative adjustments. This paper provides a foundation for future research on aligning Taiwan's legal framework with GRP principles, considering its unique blend of US and German legal influences. The findings have broader implications for understanding the complexities of implementing GRP across diverse legal and administrative systems globally.

Liu, Han-Wei, From CPTPP to U.S.-Taiwan Initiative on 21st-Century Trade: The Evolution of Good Regulatory Practices and its Implications for Taiwan (August 1, 2024). Singapore Management University School of Law Research Paper Forthcoming, Forthcoming 59 (1) Journal of World Trade (Feb. 2025)
, Available at SSRN: https://ssrn.com/abstract=4954226 or http://dx.doi.org/10.2139/ssrn.4954226

Duties for Datasets

Published Date: 18/09/2024

Author(s): Damian Clifford, Jeannie Paterson, Kwan Ho Lau

Machine learning (ML) systems are increasingly being deployed in contexts, such as law, medicine and finance, where system errors present serious and foreseeable risks. As ML system behaviour is largely determined by their training inputs, should dataset providers owe duties of care to victims? Using the ImageNet dataset and the Generative Pre-trained Transformer (GPT) models as case studies, this chapter argues that the conventional approach of centralising duties on system providers alone yields insufficient safeguards. Dataset-specific duties should also be considered to incentivise precaution in the preparation of crucial ML input. The chapter analyses how dataset duties may be encompassed in existing tort law, surfacing situations where duties are more appropriate. For instance, where a dataset is intended to be used in a risky context, the dataset provider actively influences system outputs, and the dataset is published without safety restrictions or warnings.

Soh, Jerrold, Duties for Datasets (June 01, 2023). Damian Clifford, Jeannie Paterson & Kwan Ho Lau eds, Data Rights and Private Law (Hart Publishing, 2023) pp. 207-223, Available at SSRN: https://ssrn.com/abstract=4928051 or http://dx.doi.org/10.2139/ssrn.4928051

The Treatment of Digital Assets in Insolvency

Published Date: 25/08/2024

Remolina, Nydia and Gurrea-Martínez, Aurelio and Liu, Daniel, The Treatment of Digital Assets in Insolvency (August 04, 2024). Oxford Handbook of Digital Assets and the Law (Forthcoming, Oxford University Press, Jason G. Allen, Simon Gleeson, and Peter Hunn eds)  , Available at SSRN: https://ssrn.com/abstract=4915592 or http://dx.doi.org/10.2139/ssrn.4915592

Generative artificial intelligence: The protection of personal data and countering false narratives about the person

Published Date: 01/07/2024

Author(s): Warren B. CHIK

Generative artificial intelligence (“Gen AI”) has rapidly become ubiquitous on online platform services, from chatbots and virtual assistants to search engines and social media. This generated concerns over potentially harmful effects from its use in both social and professional settings, including the added threats to personal data privacy and accuracy of personal information. In this article, the author will explain how Gen AI operates and why it gives rise to these issues, examine the policy and law relating to Gen AI, both existent and anticipated, and suggest possible solutions to the problems in the form of legal and non-legal measures.

CHIK, Warren B.. Generative artificial intelligence: The protection of personal data and countering false narratives about the person. (2024). Singapore Academy of Law Journal. 36, 307-345.
Available at: https://ink.library.smu.edu.sg/sol_research/4587

Legal governance of artificial intelligence in healthcare devices and services in Singapore

Published Date: 01/07/2024

Author(s): Warren B. CHIK, Haran SUGUMARAN

The use of artificial intelligence (AI) in healthcare applies across a spectrum of goods and services that support the industry. The main benefits of developing and applying AI to healthcare are: (a) the potential for it to perform functions beyond what can be done manually by the natural person; (b) the greater efficiency and accuracy that it can give to detection, diagnosis and medication/recovery; (c) the minimisation of risk and the possible lowering of cost (in the long term); and (d) in appropriate cases, minimal human intervention or involvement, especially in situations or conditions that may present a risk of transmission or infection. Advancements in healthcare AI are also vital to the social well-being and economic development of a country, as it can both minimise costs to society and boost the healthcare sector. Although the tendency is to discuss healthcare AI in the context of healthcare devices or medicine/treatment, AI can have broader uses, such as providing administrative support and acting as a clinical decision-making tool (with or without human oversight). This is the approach that Singapore’s government has taken to improving the delivery of healthcare services, as seen in the Singhealth–SGInnovate Memorandum of Understanding.

CHIK, Warren B. and SUGUMARAN, Haran. Legal governance of artificial intelligence in healthcare devices and services in Singapore. (2024). Research handbook on health, AI and the law. 373-391.
Available at: https://ink.library.smu.edu.sg/sol_research/4589

AI Characterisations and their Legal Implications

Published Date: 20/06/2024

Author(s): Jerrold Soh

This Chapter examines the difficult legal characterisation problems that artificially-intelligent (AI) systems raise and explores how different characterisations of AI shape practical legal outcomes. Three reasons are offered for the legal difficulty with characterising AI. First, answers to characterisation problems are inherently subjective and perspective-driven, particularly when the subject is an intangible technological system. Second, AI technology is especially difficult to define since the field typically proceeds on inexact anthropomorphic metaphors. Third, AI characterisation problems raise difficult sub-problems, particularly in determining how autonomous an AI system is. The Chapter thus argues that a range of plausible AI characterisations will inevitably exist for lawyers to choose from, as demonstrated by the plethora of things, entities, and persons scholars have likened AI to. In turn, these choices shape how we think about regulating and assigning responsibility for AI systems. Here the point is substantiated with case studies of emerging AI regulations and recent litigation involving AI systems in commercial, defamation, and intellectual property contexts. The Chapter concludes by reiterating the inherent malleability of AI characterisations and calling on regulators and adjudicators to be more deliberate in choosing characterisations aligned with their intended legal outcomes.

Soh, Jerrold, AI Characterisations and their Legal Implications (May 24, 2024). forthcoming in the Research Handbook on the Law of Artificial Intelligence (2nd Ed), Singapore Management University School of Law Research Paper Forthcoming, Available at SSRN: https://ssrn.com/abstract=4840200

AI at the Bench: Legal and Ethical Challenges of Informing - or Misinforming - Judicial Decision-Making Through Generative AI

Published Date: 16/06/2024

Remolina, Nydia and Socol de la Osa, David, AI at the Bench: Legal and Ethical Challenges of Informing - or Misinforming - Judicial Decision-Making Through Generative AI (June 11, 2024). Available at SSRN: https://ssrn.com/abstract=4860853 or http://dx.doi.org/10.2139/ssrn.4860853

The spread of synthetic media on X

Published Date: 02/06/2024

Corsi, G., Marino, B., & Wong, W. (2024). The spread of synthetic media on X. Harvard Kennedy School (HKS) Misinformation Review. https://doi.org/10.37016/mr-2020-140

The Transatlantic Divide: Intermediary Liability, Free Expression, and the Limits of Trade Harmonization

Published Date: 20/05/2024

Author(s): Han-Wei Liu

Amid escalating apprehensions surrounding content regulation, the USA has discreetly integrated provisions reminiscent of its Communications Decency Act Section 230 (CDA 230) into trade agreements, offering broad immunity. This scholarly analysis critically assesses this maneuver by juxtaposing such CDA 230-like provisions against the UK’s established legal framework governing online content and freedom of expression. Utilizing a comparative legal methodology, the paper underscores the pronounced differences between the USA and UK stances on intermediary liability for third-party content, molded by their unique constitutional foundations and jurisprudential interpretations of free speech rights. The insertion of CDA 230-aligned clauses into trade agreements poses a potential threat to the UK’s nuanced equilibrium between safeguarding free speech and upholding other paramount interests, such as privacy and reputation. An scrutiny of UK defamation statutes and content regulation protocols reveals inherent challenges in transplanting CDA 230 provisions into trade contexts. In summation, the paper ardently supports a diversified approach to online content governance and cautions against standardizing intermediary liability laws via trade agreements, especially between nations with divergent foundational beliefs. It fervently endorses a cross-disciplinary discourse involving both trade and legal specialists to ensure the preservation of free expression while concurrently recognizing the intricacies of crafting universally applicable standards for online platforms and content regulation.

Liu, Han-Wei, The Transatlantic Divide: Intermediary Liability, Free Expression, and the Limits of Trade Harmonization (September 10, 2023). International Journal of Law and Information Technology, Volume 31, Issue 4, Winter 2023, Pages 376–398, Available at SSRN: https://ssrn.com/abstract=4787252

NLP in the Legal World

Published Date: 11/04/2024

Author(s): Jerrold Soh

This talk situates the rising field of NLLP in the context of legal scholarship and emerging trends in legal AI practice and regulation. It centrally suggests that, as NLP's domain of competence expands, it would have to undergo, and is in several ways already undergoing, a fundamental transformation we might refer to as “growing up”. In particular, to succeed in the legal world, NLP technology has to contend with three key aspects of adulthood: new attitudes, new consequences, and new responsibilities. Lawyers have gone from complete AI skepticism to actively exploring use cases. Encroaching into fields like medicine, law, and finance means technologists cannot avoid dealing with difficult questions around protecting life, liberty, and money. An entire new AI rulebook is currently being written by regulators and courts around the world. Against this backdrop, the talk examines how NLLP relates to existing inquiries in computational law, AI and Law, and computational/empirical legal studies and identifies opportunities for inter-field discourse. It concludes by identifying the unique role that NLLP researchers can play in the increasingly controversial (and seemingly, decreasingly scientific) global debate on the use and regulation of large language models.

Soh, Jerrold, NLP in the Legal World (December 7, 2023). Available at SSRN: https://ssrn.com/abstract=4670517 or http://dx.doi.org/10.2139/ssrn.4670517

Comparing Smart City Data Protection Approaches: Digital Consent and the Accountability Framework in Singapore

Published Date: 05/04/2024

Author(s): Wenxi Zhang, Sharanya Shanmugam, and Jason G Allen

Traditionally, there has been heavy reliance on formal notice and consent models for data protection regimes in smart cities such as Singapore. However, consent (in particular digital consent) is rarely extended meaningfully in practice, due to behavioural factors including data subjects’ limited cognitive capacity, rational apathy, and dependency on the digital infrastructure around them. Synthesising legal perspectives with insights drawn from the behavioural and social sciences, this paper analyses this problem in the smart city context–where it is exacerbated due to prevailing issues such as economic disincentives for ensuring meaningful consent, since essential urban infrastructure is often provided by private companies. In recent years, such phenomenon has raised concerns among policymakers in some jurisdictions and has given rise to a shift in approach to data protection. Recognising the shortcomings of what we call the Consent Model, Singapore has enacted new amendments to its Personal Data Protection Act (PDPA) in 2021, shifting towards what we call an Accountability Model, that repositions the burden of data management away from data subjects and towards organisations. Without necessarily meeting the problems of the Consent Model, critics of this alternative approach argue that it tends to be business-oriented and the interests of data subjects could be overshadowed. This paper argues that Singapore’s PDPA is in fact a Hybrid Model. While emphasising the value of Accountability from data-holding organisations, it also recognises the importance of the Consent Model where appropriate in the smart city. On this basis, through an interdisciplinary lens this paper proposes ways of enhancing both the Consent and Accountability components of the Singaporean data governance regime. It recommends specific policy and legal reforms, including introducing a participatory component to the Accountability Model to ensure that data subjects' voices are effectively incorporated into the formulation and implementation of data protection policies.

Zhang, Wenxi and Shanmugam, Sharanya and Allen, Jason G, Comparing Smart City Data Protection Approaches: Digital Consent and the Accountability Framework in Singapore (November 30, 2023). SMU Centre for AI & Data Governance Research Paper No. 02/2024, Available at SSRN: https://ssrn.com/abstract=4785278 or http://dx.doi.org/10.2139/ssrn.4785278