Background
As Southeast Asia’s tech front runner, it comes as no surprise that Singapore is ahead of the pack among its ASEAN peers in the AI landscape. The city-state’s rapid advancements and agile approach to AI technical development and policymaking have been noted across the Asia Pacific. Singapore’s geographic size, multicultural mix, and reputation as the melting pot of technological innovation in the region makes it the ideal AI laboratory, capturing the imagination and even the admiration of other countries like Australia, Japan, and South Korea. As Singapore rides the momentum of its technological advancements in AI, it has also started to install guardrails to maximize the benefits and mitigate any unintended harms.
In June 2018, Singapore announced that it would embark on three AI Governance and Ethics Initiatives:286 (1) an Advisory Council on the Ethical Use of AI and Data, established in August 2018 and comprising of government and private sector representatives; (2) a discussion paper released by the Personal Data Protection Commission (PDPC) on responsible development and adoption of AI, which will be used by the Council to frame its deliberations; and (3) a research program on the governance of AI and data use to advance and inform scholarly research on AI governance issues.
In 2019, Singapore launched the National AI Strategy (NAIS), under the Smart Nation and Digital Government Office’s National AI Office. As part of a three-point vision to use AI to transform the country’s economy and the lives of its people, NAIS will deploy AI at a national scale for Singapore to become a global hub for AI solutions; generate new business models and deliver innovative services to local populations; and train the Singaporean workforce to adapt in the evolving knowledge-based economy.287 To build a viable AI ecosystem, NAIS outlined seven national AI projects, namely, healthcare, smart estates, education, border security, logistics, finance, and government,288 which will be supported by key enablers—multi-stakeholder partnerships, data architecture, talents and education, trusted environment, and international collaboration.289
Singapore’s Smart Nation initiative is a culmination of previous efforts to digitize public service delivery or e-government.290 But unlike previous attempts, this Smart Nation initiative aims for a complete digital transformation across different aspects of urban life.291 The impetus for the Smart Nation initiative to employ smart technologies is rooted in the confluence of internal and external events. Globally, there is an emerging trend to capitalize on the value creation offered by big data. At the same time, domestically, the political debate on urban development has been intensifying.292 High population density and immigration were two factors that led to growing public discontent, which resulted in a decline in support for the ruling People’s Action Party in the 2011 general elections.293 The juxtaposition of the economic opportunities from the global explosion of data, combined with growing public pressure from the Singaporean electorate to address the challenges of overcrowding in the city-state, compelled the government to lay the early foundations of the Smart Nation initiative.294
Usage and Impact
Investments in research and development and human resources were integral to achieving Singapore’s Smart Nation ambition. Deputy Prime Minister and Coordinating Minister for Economic Policies Heng Swee Keat announced in November 2019 that the government was investing SGD500 million (around USD364 million) to fund AI projects under the Research, Innovation and Enterprise 2020 plan.295 In affirming Singapore’s vision of becoming a global AI hub by 2030, Heng said that the island republic would strive to be at the forefront of developing and deploying scalable and impactful AI solutions.296 Singapore’s ambition to lead in AI innovation is warranted. McKinsey has found Singapore to be the frontrunner in AI experimentation in financial services, high-tech telecommunications, manufacturing, and mobility in Southeast Asia.297 Accenture estimates that AI has the potential to boost Singapore’s annual economic growth from 3.2 percent to 5.4 percent and its labor productivity by 41 percent by 2025.298
Over time, the number of key enablers of Singapore’s digitization efforts under its Smart Nation program has expanded, underpinned by a strong collaboration between government, industry, and academia.299 For instance, the Monetary Authority of Singapore (MAS) established a regulatory sandbox in collaboration with PayPal to incubate start-ups in financial technology. The collaboration produced PolicyPal, an AI-powered app which facilitates registration of insurance policies.300 Since then, the regulatory sandbox has been adopted as a useful policy tool in Singapore’s smart city initiatives to co-create solutions among relevant government agencies, the private sector, and policymakers.301
NAIS also introduced the idea of building data lakes to manage Smart Estates in Singapore. Smart Estates refer to the deployment of smart technologies to collect, analyze and optimize the use of data to pre-empt problems or predict trends in housing estates. Through Smart Estates, the Singaporean government can tap data to identify the use of electricity for lighting and cooling systems.302 The Infocomm Media Development Authority (IMDA) and the Singapore University of Technology and Design will pilot data lakes to improve data access and serve as a repository for common data standards and governance frameworks. With the malleable nature and emergent effects of AI, NAIS remains a living document, open to constant review to reflect the evolving technical, ethical, and socio-economic dimensions of AI.
Case Study
Use of AI in Urban Planning
In the Singaporean government’s efforts to construct a Smart City, smart lamp posts have been planned for installation throughout the city. The lamp posts are to house a host of interconnected sensors and cameras belonging to different government agencies for detecting everything from unruly crowds and speeding e-scooters, to hazy weather. AI technologies such as big data analytics and deep learning would analyze the integrated pool of data collected to facilitate better-informed and co-ordinated public sector decision-making in urban planning.
Concerns were raised about the surveillance cameras and facial recognition technologies that would be incorporated into the smart lamp posts, but Prime Minister Lee Hsien Loong stated that the project was aimed at improving the lives of citizens, and that it was not intended to be unethical or intrusive. A spokesman for GovTech said: “The need to protect personal data and preserve privacy are key considerations in the technical implementation of the project.” In order to ensure that data collected remain secure, the government has engaged commercial security stakeholders such as LogRhythm’s NextGen SIEM platform to monitor and detect potential cybersecurity threats.303 The platform allows for all data sources, including cameras, sensors, cloud network, servers, and security operations centers’ workstations, to be integrated onto a single platform, enabling the security team to identify high-risk activities in the network and corroborate threat indicators.
To address the governance conundrum in AI development, Singapore prides itself on being the first country in Asia to put forward an AI framework.304 The Model AI Governance Framework aims to address the complex relationship between innovation and regulation. Oriented around internal and external governance measures, risk-management, and operation management, the Model AI Governance Framework provides policymakers and industry practitioners with practical tools to address and overcome future AI challenges, given the disruptive nature of technology.305 306
In building trust toward AI, the Framework is grounded on two high-level principles. First, AI solutions’ decision-making process should be explainable, transparent, and fair. Second, they should be “human-centric.”307 Organizations must help people understand how machine learning and deep learning make their predictions. They must also oversee the overall process as to how AI models use data to make predictions and arrive at fair assessments or outcomes. Human-centric refers to the requirement that AI should benefit the well-being and safety of society.308
To further guide industry in adopting a self-regulatory approach to the development, deployment, and usage of AI, the Singaporean government produced the Compendium of Use Cases and the Implementation and Self-Assessment Guide for Organisations (ISAGO) companion document.309 Both documents provide practical examples to help organizations align their AI governance practices to the Model Framework.
As part of its industry outreach, the MAS entered a multi-phase collaborative project with the financial industry to evaluate their Artificial Intelligence and Data Analytics (AIDA), and to co-produce principles of fairness, ethics, accountability, and transparency which can be applied to banking (credit risk scoring and customer marketing) and insurance (predictive underwriting and fraud detection).310
Singapore is also making its mark in the emerging AI international standards setting arena, participating in the ISO/IEC JTC 1/SC 42 standards committee on Artificial Intelligence.311 As of the time of writing, in Southeast Asia only Singapore and Indonesia were involved in the committee, with Singapore participating as a full member with voting rights and Indonesia as an observer member. With its high technical capacity and heavy investments in AI research and development, Singapore can utilize its expertise to contribute productively to the ongoing global discussions on standards.312
Challenges and Prospects
Singapore has secured the first mover advantage in AI development in the region, but a few challenges remain. These obstacles include a shortage of talent; repeated incidents of data breaches; the government’s highly centralized approach; and problems with upholding transparency, inclusion, and equity.
AI workforce
To elevate its competitive position in the AI landscape, Singapore must contend first with the lack of a capable workforce. It is projected that Singapore’s talent deficit will amount to 600,000 in the next few years.313 Although the government has launched apprenticeships, graduate scholarships, and conversion programs with the private sector, Singapore will have to find creative ways to attract international talent.314 If not, the city-state risks losing approximately USD107 billion by 2030 due to manpower shortages.315 While off-the-shelf AI solutions remain a viable option, AI is an artisanal endeavor that requires a “human-in-the-loop” to ensure the explainability and reliability of its models. Humans in the loop can inspect, verify, and alter algorithms at different stages of the cycle to make high quality AI models which embody fairness and transparency. Highly skilled and capable talent is fundamental to harnessing the power of AI to mitigate any potential harm or bias.
Cyberattacks
High profile incidents of data breaches involving Singapore’s digital native companies deploying AI solutions like Grab can dampen public confidence in AI.316 The repeated occurrence of data privacy breaches can have a chilling effect on public-private partnerships involving data usage in pilot testing, regulatory sandbox, and the establishment of infrastructures like data lakes. Cyberattacks through adversarial AI are an emerging threat that further challenge the efficacy of current data protection laws. Defense Minister Ng Eng Hen has called for the need to revisit cybersecurity standards and frameworks to guide the public and the private sectors with the increasing integration of AI and big data.317 Singapore’s private sector has echoed similar sentiments, noting that increasing reliance on technology will result in more cyberattacks.318 The recent public backlash over the TraceTogether app and other high-profile data breaches adds to the growing reluctance among Singaporeans to share personal information with the government.319
Surveillance, accountability, and public trust
Recognizing the public’s growing skepticism about the reach and impact of AI and big data, combined with deteriorating confidence in data protection, it could be hard for the Singaporean government to solicit more solid public buy-in in further rollouts of Smart Nation initiatives as the public starts to focus more on deeper issues of surveillance, accountability, public trust, and inclusion.
Singapore’s use of facial recognition and smart city solutions predates its smart city initiatives. With the deployment of facial verification and smart lamps as components of crowd analytics, there is growing criticism of how facial recognition technologies can inadvertently facilitate discrimination based on gender and ethnic bias.320 The intrusive nature of such technologies extending to human emotions is also problematic, particularly in the absence of user consent.321
Relatedly, there is also the issue of accountability and public trust. Despite claims that the Model AI Governance Framework is sector-, technology-, and algorithmic-agnostic—meaning it has a general focus on AI data analytics, systems, and software and applies as a standard baseline for organizations across all sectors322—it is still considered weak. As adoption remains voluntary in the private sector, there is no clear-cut understanding of the extent to which it will be applied.323 With its non-binding nature, the framework can be applied during the initial stages of tech deployment, but AI developers can eventually depart from it in the process. Thus, more concrete oversight must be adopted to address safety considerations, given the rapid integration of AI-based features in commonly available applications.324
With these concerns simmering, there is a clear need for concrete legislation to ameliorate the unintended effects of AI. The Law Reform Committee of the Singapore Academy of Law has called for a more proactive approach to tackle developing, deploying, and scaling AI applications. It published a series of reports that called for the passage of “soft laws” to develop AI technologies “that foster socially and economically beneficial development and use of robotic and AI-driven technologies.”325 Specifically, the reports highlight the potential risks of autonomous robotics and AI (RAI) on humans and property, and issues over how existing criminal laws and liability can be applied. In conclusion, the Committee recognized that RAI could give rise to new forms of harm, thus inevitably challenging existing laws and regulations. This will, in turn, require all regulators and legislators to be agile in addressing new and emerging risks.326
Underlying issues of weak regulatory and legislative frameworks lead to legitimate concerns over surveillance and discrimination, and this will have far-reaching implications in terms of Singaporeans’ perception of AI. A Pew survey confirms that, overall, 72 percent of Singaporeans perceive AI as beneficial to their society, but the use of robots to automate jobs solicited mixed views, with 48 percent saying it would be a good thing and 42 percent averse to the idea.327 The mixed results show that it remains to be seen how the actual implementation of AI technologies can live up to its promise.328
Inclusion and equity
As mentioned, the IMDA established an Advisory Council on the Ethical Use of AI and Data to support the establishment of a trusted AI ecosystem.329 This is a positive step toward potentially bridging any gaps in inclusion and trust among its diverse members.330 But experts argue that the government’s heavy hand in guiding the council’s direction still casts a shadow of doubt over its capacity to genuinely dive into the root cause of AI’s potential and unintended risks without being influenced by issues of politics and profit.331 In various stakeholder consultations conducted for this report, informants cautioned that the government’s tendency toward heavy technological determinism should be balanced by a deeper sense of critical reflection to promote inclusion and equity.
The government’s top-down digitization efforts have crowded out private-sector opportunities, which disproportionately impacts small players, entrepreneurs, and start-ups.332 The consequences of the government’s centralized approach are already manifest—the Smart Nation initiative is struggling to capture Singaporeans’ imagination due to the lack of other success stories besides its local fintech industry.333 Setting up adequate digital infrastructure and providing funding resources are essential, but the government should allow start-ups to “play” and exercise a relative degree of creative freedom to spur out-of-the-box innovation.334
Small and Medium Enterprises (SMEs) continue to face digitalization barriers in Singapore. For context, Micro, Small and Medium Enterprises (MSMEs) comprise 99 percent of all firms, employ 72 percent of the workforce, and contribute approximately 50 percent to the total GDP. Although SMEs in the city-state are optimistic about their digital transformation,335 it was found that they continue to experience higher costs in tech adoption, an urgent requirement to upskill employees, and the need to raise cybersecurity spending against potential risks.336 Singapore could also pay more attention to the role of women-led businesses. Despite the government’s campaigns for digital inclusiveness, such as Digital for Life,337 and women-focused initiatives such as SG Women in Tech,338 more could be done to close the gender imbalance. A study conducted by Accenture revealed that women-owned businesses account for only 27 percent of all businesses and 13 percent of sales.339 Addressing gender parity through more opportunities for women-owned businesses could yield an additional 20 percent to Singapore’s GDP.340
Another dimension of exclusion that pervades AI development in Singapore is the notion of data dominance.341 Most often, Singapore is perceived as a highly interconnected city-state capable of capturing huge streams of data. But a major concern among experts is the overrepresentation and the underrepresentation of specific groups or profiles of individuals and communities in Singapore that can lead to some being marginalized or excluded. The term data dominance has a sharper edge when discussing data inclusion or exclusion, because it shows how stereotypes and binaries can be reinforced in the digital realm with overrepresentation or underrepresentation in data sets.
Singapore is comprised of diverse communities imbued with an array of different ethnicities and faiths. In addition, it is also home to thousands of foreign talents engaged in both high-skilled and labor-intensive industries. With a unique cultural mix and manageable geographic size, Singapore is a living AI laboratory capable of testing, developing, deploying, and scaling AI solutions. However, if not managed carefully, data dominance can favor one community over the other and disproportionately impact those who are already in precarious positions.342
The TraceTogether controversy demonstrated how AI-backed platforms can impact vulnerable migrant workers in cosmopolitan cities like Singapore in the age of globalization. Although Singapore has had one of the world’s most efficient coronavirus responses, its preferential programs aimed specifically at monitoring the mobility of migrant workers showed how foreign workers were considered a risk, rather than being at risk.343 More importantly, it illustrated how technology can reinforce or amplify racism and xenophobia, resulting in “multiple and intersecting forms of discrimination and inequality, gender-specific restrictions in migration policies, precarious and informal labor [conditions].”344
Challenges on inclusion and equity are not unique to Singapore. The problems of digital technologies perpetuating current structural power imbalances and social injustices have gained wider attention, alongside a wave of renewed optimism on AI.345 With AI’s unprecedented integration and the dawn of an emerging digitally-centric society, redefining what it means to be a citizen in this new era is paramount. As one informant argued, being a digital citizen requires knowing how to be human and humane in today’s digital societies and understanding one’s digital rights. Having such knowledge will allow human beings to live and thrive with machines that possess human-like abilities. Reflecting on what it means to be a digital citizen will demand more than just a checklist or guidelines but more space for dialogue and deliberation.346
Informants suggested establishing a “social material assembly”–informed by the socio-technical approach—to fully harness the dynamics of human-machine interaction and to alleviate concerns about surveillance and discrimination.347 In practical terms, the social material assembly could be implemented by convening community forums to facilitate better interaction and understanding between the creators and innovators of AI technologies, as well as direct users and the general population more broadly.348 Community dialogues or forums would open more direct communication channels between technical experts, like AI engineers and data scientists, as well those who come from non-technical backgrounds.349 These forums could help technical experts better grasp the implications of their codes or models in real-world settings, while the non-technical individuals could provide feedback.350
The assembly would also incorporate the philosophical approaches of mindfulness and clarity with high technical capacity. Clarity includes knowing what kind of information an individual needs to guide his or her behavior, while mindfulness involves leading AI and not being led by it.351 Dialogues or deliberations are key in translating clarity and mindfulness into concrete terms and in realizing digital equity.352 Achieving digital equity will help transcend not only the gap between the haves and have-nots in tech access, but also the generational divide between Singapore’s ageing population and younger digital natives.353
It is not sufficient for the private sector to drive the conversation on the future of AI. Instead, proactively involving citizens in such deliberations in an organic fashion could facilitate more buy-in and ownership.354 Combined with ongoing digital literacy and education, internalizing what it means to be a digital citizen could be the key to a more ethical and sustainable AI in the future.355
Conclusion
After a careful examination of Singapore’s approach to data privacy and security and AI standards, it is apparent that there is a fundamental connection between the two. As the building blocks of AI, Singapore ensures that the foundational elements of confidentiality, integrity, and accessibility of data are intact and, more importantly, that the individual’s rights to privacy and security are protected. Although the Model AI Governance Framework is non-binding, the stringent regulatory measures offered by the PDPA lay the foundation to ensuring that organizations comply with their data protection obligations in processing personal data. From a practical standpoint, the PDPA serves as a guiding framework to help companies set up accountability-based practices in data management and protection. If companies do not align their policies, structures, and processes around the core tenets of PDPA, they risk legal, ethical, and governance issues which may erode public confidence in AI. The enforcement of the PDPA thus provides concrete assurances that AI technologies built or utilized in Singapore are held to high-level standards.
A cursory glance at the current data landscape in the region shows a rising trend of adopting the EU’s GDPR as the gold standard for data privacy and protection. While this signals a positive step, a major concern on the horizon is how to reconcile such international frameworks with developments in Southeast Asia; in particular, whether a regional and interoperable AI ecosystem can be cultivated in the region amid tensions between data localization, on the one hand, and cross-border data flows, on the other. Between the two camps, Singapore leans more toward the free and open flow of data, based on its various bilateral digital economic agreements and membership of regional trade frameworks.
The recent launch of the Global Cross-Border Privacy Rules (CBPR), building on the existing APEC CBPR and Privacy Recognition for Processors, could provide ASEAN with a middle pathway.356 The membership of Singapore and the Philippines in the Global CBPR could provide the opportunity for dialogue among other ASEAN members that are also members of APEC and subscribe to the existing APEC CBPR. Finding a nimble and win-win arrangement to facilitate equivalency measures or reciprocity on data flow could help the region lay the groundwork for an interoperable AI ecosystem.
As Singapore continues to journey in the uncharted territory of AI, and in the wider context of the data-driven economy, its ambition to become a global hub could be enhanced by inclusive and flexible public engagements and education. Existing efforts on digital literacy and information and communication campaigns could be supplemented by deep and meaningful conversations that ask the more practical yet pertinent questions of its citizens: “Why should I care?” and “How should my understanding drive my decisions?”. As Singapore aims to become the hub of AI innovation in the region—whether through exporting advanced AI technologies, or leading rule-making strategies on AI governance in international standards setting or policymaking—it will have to contend with increasing tension between innovation and regulation. But ultimately, according to our informants, Singapore will need to wrestle with the more uncomfortable issues and challenges of digital equity, digital citizenship, and AI sustainability, which its top-down approach has not yet adequately addressed.
Endnotes
286 “Artificial Intelligence Governance and Ethics Initiative,” Infocomm Media Development Authority, June 5, 2018.
287 “National Artificial Intelligence Strategy: Advancing our Smart Nation Journey,” Smart Nation Singapore, November 2019.
288 Ibid.
289 Ibid., 7-8.
290 Chee Wee Tan and Shan L Pan, “Managing e-transformation in the public sector: an e-government study of the Inland Revenue Authority of Singapore (IRAS),” European Journal of Information Systems 12, 269-281 (2003); Krishnamurthy Sriramesh and Milagros Rivera-Sanchez, “E-government in a corporatist, communitarian society: the case of Singapore,” Sage Journals, (October 1, 2006).
291 Jun Jie Woo, “Technology and Governance in Singapore’s Smart Nation Initiative,” Ash Center Policy Briefs Series, Harvard University, Cambridge, MA, 2018.
292 Linda Y C Lim, World Scientific Series on Singapore’s 50 Years of Nation-Building: Singapore’s Economic Development: Retrospection and Reflections (University of Michigan, February 2016); Kent E. Calder, “Singapore: Smart City, Smart State,” Brookings Institution Press, (2016); Jun Jie Woo, “Technology and Governance in Singapore’s Smart Nation Initiative.”
293 Derek da Cunha, Breakthrough" Roadmap for Singapore’s Political Future, (Singapore: Straits Times Press, 2012); Jun Jie Woo, “Technology and Governance in Singapore’s Smart Nation Initiative.”
294 Derek da Cunha, Breakthrough” Roadmap for Singapore’s Political Future; Kenneth Paul Tan, “Singapore in 2011: A ‘New Normal’ in Politics?,” Asian Survey 52, (2012).
295 Tang See Kit, “Singapore rolls out national strategy on artificial intelligence for ‘impactful’ social, economic benefits,” CAN, November 13, 2019.
296 Ibid.
297 Sachin Chitturu, Diaan-Yi Lin, Kevin Sneader, Oliver Tonby, and Jonathan Woetzel, “Artificial Intelligence and Southeast Asia’s Future,” McKinsey Global Institute, September 2018.
298 “AI is the Future of Growth,” Accenture, 2017.
299 Jun Jie Woo, “Technology and Governance in Singapore’s Smart Nation Initiative.”
300 Yasmine Yahy, “Insurance start-up PolicyPal graduates from MAS fintech regulatory sandbox,” The Straits Times, August 29, 2017.
301 Julien de Salaberry, “The Case for a HealthTech Regulatory Sandbox in Singapore,” Galen Growth Asia, February 10, 2017.
302 Yip Wai Yee, “IMDA partners Austrade to develop smart estates solutions for the community,” The Straits Times, June 4, 2019.
304 Nydia Remolina and Josephine Seah, “How to Address the AI Governance Discussion? What Can We Learn from Singapore’s AI Strategy?,” SMU Centre for AI & Data Governance, Research Paper No. 2019/03, (July 19, 2019.
305 Ibid.
306 “Model Artificial Intelligence Governance Framework: Second Edition,” Info-communications Media Development Authority and Personal Data Protection Commission, 2020.
307 “Model Artificial Intelligence Governance Framework: Second Edition,” Info-communications Media Development Authority and Personal Data Protection Commission, 15.
308 Ibid.
309 “Singapore’s Approach to AI Governance,” Personal Protection Data Commission Singapore, June 1, 2022.
310 “Veritas Initiative Addresses Implementation Challenges in the Responsible Use of Artificial Intelligence and Data Analytics,” Monetary Authority of Singapore, January 6, 2021.
311 Dr. Peter Lovelock, Dr. Peter leong, Dr. Jasmine Begum, Jishu Basak, “Artificial Intelligence Standards and Trade in ASEAN,” IIC Webinar, Singapore Chapter, February 25, 2021.
312 Ibid.
313 Joe Devanesan, “Is Singapore facing a tech talent crunch?,” Techwire Asia, September 22, 2020.
314 Manoj Harjani, Dymples Leong, and Teo Yi-Ling, “Artificial Intelligence: Sustaining Singapore’s AI Ambitions,” RSIS, November 24, 2020.
315 “Future of Work: The Global Talent Crunch: Country Perspective: Singapore,” Korn Ferry, 2018.
316 Lester Wong, “Grab fined $10,000 for fourth data privacy breach in S’pore in two years,” The Strait Times, September 14, 2020.
317 Lim Min Zhang, “Rules urgently needed for cyber, AI and other emerging domains amid growing threat of cyber attacks: Ng Eng Hen,” The Straits Times, October 12, 2021.
318 Ibid.
319 Jun Jie Woo, “Technology and Governance in Singapore’s Smart Nation Initiative.”
320 Manoj Harjani, “Facial Recognition: More Peril than Promise,” RSIS, February 10, 2021.
321 Ibid.
322 “Model Artificial Intelligence Governance Framework: Second Edition,” Info-communications Media Development Authority and Personal Data Protection Commission, 10
323 Manoj Harjani, “Singapore’s AI ‘Living Lab’: Safety Rules Essential,” RSIS, September 28, 2021.
324 Ibid.
325 Report on Criminal Liability, Robotics and AI Systems, (Singapore Academy of Law: Law Reform Committee, 2021).
326 Ibid., 46
327 “Public Views About Science in Singapore,” Pew Research Center, September 29, 2020.
328 Ibid.
329 “Composition of the Advisory Council on Ethical Use of Artificial Intelligence (“AI”) and Data,” Infocommunication Media Development Authority, May 26, 2019.
330 “Annex A: Council Members of the Advisory Council on the Ethical Use of AI and Data,” Infocommunication Media Development Authority.
331 Stakeholder consultation.
332 Tan Weizhen, “The Big Read: Speed bumps hinder Singapore’s Smart Nation drive,” Today, April 14, 2017; Jun Jie Woo, “Technology and Governance in Singapore’s Smart Nation Initiative.”
333 Jun Jie Woo, “Technology and Governance in Singapore’s Smart Nation Initiative.”
334 Stakeholder consultation.
335 Gerald Tjan, “What Are the Challenges and Opportunities Ahead for Singapore SMEs?,” Sales Force, April 7, 2022.
336 Atiqah Mokhtar, “Singapore SMEs lead US and UK in tech adoption: Xero,” The Edge, November 22, 2021.
337 “What’s Happening,” Infocomm Media Development Authority, accessed June 5, 2022.
338 “SG Women in Tech,” SG Women in Tech, accessed June 5, 2022.
339 “Businesswomen Grow Economies Singapore is Next: the S$95 billion opportunity,” Accenture, 2020.
340 Ibid., 4.
341 Stakeholder consultation.
342 Stakeholder consultation.
343 Jenna Hennebry and Hari KC, Quarantined? Xenophobia and migrant workers during the COVID-19 pandemic (Geneva: International Organization for Migration, 2020), 4-5.
344 Jenna Hennebry and Hari KC, Quarantined? Xenophobia and migrant workers during the COVID-19 pandemic, 5
345 Stakeholder consultation.
346 Stakeholder consultation.
347 Stakeholder consultation.
348 Stakeholder consultation.
349 Stakeholder consultation.
350 Stakeholder consultation.
351 Stakeholder consultation.
352 Stakeholder consultation.
353 Stakeholder consultation.
354 Stakeholder consultation.
355 Stakeholder consultation.
356 “Global Cross – Border Privacy Rules Declaration,” U.S. Department of Commerce.
