TPRC 2024: The 52nd Research Conference on Communications, Information and Inter

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Abstract:
The issue of cross-border data flow is increasingly central given the crucial role data plays as the lifeblood of economic and social interactions (OECD, 2020). With the rise of digitalization and cross-border transactions, concerns about national and cyber security have become more pronounced (Pangestu, M. and H. Lee-Makiyama, 2019). To address these challenges, many advocate for cross-border data to adhere to privacy and security laws or standards, which can be more effectively enforced through data localization policies. These policies may entail requirements for certain types of data to be stored in local servers and mandates for local data processing (OECD, 2020).
The digital economy thrives on the currency of data, which fuels transactions for both big tech and homegrown tech companies alike. As digital transactions increasingly transcend borders, facilitated by cross-border data flows, they significantly shape the landscape of digital marketplaces. This process involves the monetization of data, enriching global marketplaces, and enabling personalized services, particularly benefiting emerging nations in their digital advancement.
Drawing from Varian's concept of network externalities, where the value of digital products amplifies with network size, it becomes evident that a few economically powerful countries dominate digital platforms and services, leaving others as users or secondary innovators. This power asymmetry, coupled with limited computational capabilities in developing nations, results in a lack of control over the data generated by their residents.
The challenge of cross-border data flows lies in balancing data mobility, personal privacy and security, and the process of data monetization. Drawing an analogy to the Mundell-Fleming Trilemma in Keynesian Macroeconomics, we argue that national regulatory regimes face similar trade-offs. Just as a country cannot simultaneously peg an exchange rate, maintain an independent monetary policy, and allow free cross-border financial flows, in the realm of data management, a nation cannot uphold personal data privacy and security while maintaining independent data monetization mechanisms and consenting to unchecked cross-border data flows. Choices in one domain necessarily impact the others, highlighting the complexity of policymaking in the digital age.
We have conducted a cross-country analysis based on a unique dataset to empirically validate our hypothesis, providing insights into the dynamics of cross-border data flows and their implications for digital marketplaces and national policy frameworks.

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Since the United States' attempt to regulate TikTok in 2020 due to national security concerns, 32 countries have implemented various regulatory measures against the platform for different reasons. This study uses global TikTok regulations as a case study to investigate the types of social media threats perceived by nations and their corresponding regulatory responses. Employing thematic analysis, we examine the relationship between threat perceptions regarding TikTok and regulatory responses across different countries. Grounded in the dual attributes of content and data within social media, along with considerations of threats at both national and individual levels, we formulate a typology of national threats emanating from social media. The findings reveal that (i) countries perceiving threats to TikTok from national security-data concerns are more inclined to implement measures isolating it from critical devices, (ii) countries perceiving threats related to individual well-being-data are more prone to adopting privacy regulations, and (iii) countries perceiving threats linked to national security-content and individual well-being-content are more inclined to enforce complete bans or content regulations. These decisions are influenced by factors including domestic stability, religious convictions, and democratization.

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The rapid evolution of today’s technology landscape, driven by algorithm-driven systems (ADS), profoundly impacts everyday life and major societal functions. ADS, often perceived as mysterious or magical, are sophisticated computational methods that enable automated decision-making in various domains such as finance, employment, and social media content delivery. Despite their pervasive influence, understanding of these systems remains limited among the general public, posing risks to informed citizenship and democratic participation. This paper addresses the critical need for Algorithmic Literacy, a concept that extends beyond existing frameworks of media and digital literacy to encompass a basic proficiency in understanding how ADS function and influence personal and societal outcomes. Our research identifies a significant gap in Algorithmic Literacy among social media users, exacerbated by the increasing complexity and opacity of AI technologies. We propose a preliminary definition of Algorithmic Literacy and argue for its necessity in contemporary society. The paper outlines initial measures for fostering Algorithmic Literacy and sets a future research agenda to support these efforts. Our goal is to initiate a critical discourse on Algorithmic Literacy, calling on citizens, educators, policymakers, media professionals, and technologists to prioritize this issue to ensure informed and active participation in an increasingly algorithm-driven world.

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This study examines the posited relationship between diversity and the supply of debiased AI, using a cross-sectional survey sample of AI professionals working in Silicon Valley. The results of preliminary analyses show that increased diversity, when indicated by value diversity as opposed to demographic diversity, had a significant effect on decreased AI bias. Further, the analysis found that there was no significant difference in manufactured AI bias or the effort to debias AI attributable to the socio-demographic diversity alone, as self-reported by AI industry insiders. The study concludes with a call for much closer attention 1) to diversity in its conceptual and regulatory operationalization and 2) to conditional institutional variables in translating diversity into discernible effects. The author of this study emphasizes a preliminary nature of the findings, with suggestions for the potential areas of improvement in the future AI debate.

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This paper is a comparative analysis of how two leading developing nations, China and India, are proposing to regulate artificial intelligence (AI) systems. Despite similarity in circumstances as large developing economies aiming to upgrade their technology sectors and create jobs, the two countries have taken significantly different approaches to AI regulation. We discuss the reasons why, based on review of agency reports and documents in the two countries.

This panel compares and contrasts how regulatory agencies and approaches are emerging in different regions to address digital challenges and opportunities. The audience will learn whether and how nations decide to address problems with current resources, reform existing regulatory agencies, create new ones, or leave the problems alone. The panelists are leading authors and regulators who will describe the dynamic landscape of digital technologies, their impact on various sectors, and the role of a digital regulatory agencies for these areas. They will discuss the challenges that digital regulatory agencies face--promoting innovation and consumer protection, managing data privacy and security, and balancing free speech and misinformation. The panel will cover a wide range of topics related to a government agency’s role and responsibilities in overseeing the digital landscape. Speakers present international perspectives on how digital regulatory agencies are emerging in US, EU, Asia, Latin America and Africa.

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The boom in AI governance initiatives rests on deeply flawed understandings of digital technology and its underlying political economy. This paper rejects prevailing conceptualizations of the AI governance problem and goes so far as to reject the label “AI” as a meaningful and useful name for the object of governance. What we now call “AI” is really a globally integrated digital ecosystem composed of computing devices, digital networks, digitized data, and software programs. The article’s theme is that what we now call “artificial intelligence” is not a new technology that creates its own distinctive governance problems, but outgrowths of computing and the ecosystem of technical standards, data, devices and networks that have grown up around it. From a public policy standpoint, “AI” is an unscientific, over-simplified label for evolving applications of computing. The applications we call AI are so numerous, so diverse, and so indistinguishable from computing as to render the concept of “AI governance” meaningless.

The claim that AI doesn’t exist may seem tendentious and exaggerated, but it has the virtue of clearing the deck for a more accurate understanding of the governance implications of the digital transformation. Once we stop obsessing about “AI” and focus attention on the broader digital ecosystem, the governance problems we face are clarified. “Governing” the production and use of intelligent applications requires systemic awareness of nearly all manifestations of computing. In other words, what most people mean by “AI governance” presumes comprehensive data governance, controls on the production and distribution of semiconductors and other devices, effective Internet governance, regulation of cloud providers/platforms, and regulation of the production and distribution of software and software architectures. Further, the policy and governance problems allegedly caused by “AI” predate LLMs and chatbots and have cropped up repeatedly during the longer-term history of computing and the Internet. “AI governance” is just digital governance.

Shifting our focus to the digital ecosystem also facilitates a more realistic assessment of the necessity and proportionality of regulatory interventions. It enhances awareness of the economic and social costs of ecosystem-wide restrictions, particularly regarding freedom of expression, open competition in ICT products and services, and the ability to explore and innovate new applications of computing. Further, when it is clear that that the object of governance is the entire digital ecosystem and not some new, isolated thing called “AI,” we are in a much better position to assess what measures would be effective and how much governance is feasible in a world where heterogeneous technologies and distributed decision making are rampant, states compete for power, and no single state has supreme authority over the entire ecosystem.

The paper proceeds along the following lines. Part 1 provides a basic definition and description of the digital ecosystem and its components and explains why that conceptualization works better than various alternatives. Part 2 traces the scientific origins of the digital ecosystem and shows that cybernetic control and automation via artificial intelligence or machine learning were known to be latent in computing technology from the 1940s. Part 3 tracks the evolution of intelligent applications to show empirically how “AI” progress was tied to progressive improvement in the capabilities of all four components of the digital ecosystem, and that every one of the problems attributed to “AI” arose during the evolution of the Internet and other forms of computing. Hence, no clear line can be drawn between the governance of AI applications and the governance of the broader digital ecosystem. Part 4 evaluates some of the current proposals to “govern AI,” demonstrating they generally attempt to have the tail of AI applications wag the dog of the entire digital political economy, often resulting in ideas that either lack feasibility and/or entail extraordinary centralizations of power that could backfire on their proponents.

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The increasing integration of artificial intelligence (AI) into society raises critical questions about its impact on democracy. The development of AI governance frameworks presents a convenient and crucial ground to strengthen democracy, particularly through the lens of participatory and deliberative theories. From this viewpoint, this article seeks to explore the extent to which emerging AI governance frameworks uphold participatory and deliberative democracy. Through a comparative analysis of proposed or existing legislation in the European Union (EU), Brazil and Canada, this study investigates the specific tools and mechanisms each framework uses to involve the citizens in AI governance. The analysis reveals that while all three jurisdictions emphasise ethical governance and assessment of AI along with regulatory dialogue and multi-stakeholder collaboration, this does not effectively extend to creation of robust and specific mechanisms to facilitate citizens’ participation and deliberation. Exhibiting a stronger commitment to participatory and deliberative democracy, the Brazilian framework demonstrates a firmer position, incorporating a wider range of individual rights and direct avenues for citizen input and engagement, e.g. mandatory public consultation in advance of algorithmic impact assessment. Conversely, the Canadian and EU approaches largely rely on existing institutions and processes, overlooking the unique challenges, i.e. knowledge barriers, economic and social injustices, expert rule, that cannot be fully resolved by toolset of ethics governance or through alternative venues such as citizens’ assemblies. Overall, this study concludes by advocating for the integration of novel mechanisms that can facilitate citizen participation and deliberation within AI governance frameworks, including specifically designed and institutionalised deliberative venues.

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In its Blueprint for an AI Bill of Rights, the White House lists “notice and explanation” as one of five principles fundamental to protecting the American public as artificial intelligence (AI) is deployed. The Blueprint states “[y]ou should know that an automated system is being used and understand how and why it contributes to outcomes that impact you.” In its description of the notice/explanation principle, The White House emphasizes the importance of plain language explanations about AI use. Furthermore, a company should describe how it plans to use AI systems, how the systems work, and explain any risks to consumers. While AI transparency is associated with the possibility of auditable and accountable algorithmic systems, more research is needed to develop best practices. This project assesses the extent to which notices from 40 companies align with The White House call for AI transparency.

This study adapts a data privacy transparency assessment model to assess the AI transparency of 40 companies. The sample: 10 social media, 10 eCommerce, and 10 brick-and-mortar companies, and 10 banks. AI transparency was assessed via qualitative content analysis of AI transparency materials (via privacy policies) from company websites. Building on previous studies addressing data privacy transparency, the assessment involved assigning full, half, or zero stars on ten AI transparency criteria, including: whether transparency materials are accessible via company websites, and presented in plain language - assessed by Flesch-Kincaid grade reading level analysis, whether references to applicable laws/regulations are provided, whether information about how AI systems work and connections between AI systems and company decision-making are explained, whether the risks of AI use are explained, whether companies disclose details about data retention policies, and data storage/processing, and whether company AI transparency materials are posted elsewhere online.

Findings suggest companies provide privacy-related components of AI transparency but have yet to start disclosing details about the use and implications of algorithmic, automated systems. The average score across the 40 companies is 2.95/10 stars, with the average across social media companies (3.35/10), which is higher than e-commerce (2.75/10) and brick-and-mortar companies (2.75/10), as well as the banks (2.95/10). YouTube/Google had the highest score across the sample with 4.5/10 stars, and Alibaba and Disney+ had the fewest stars with 1.5/10. Each company sampled provided access to privacy materials via its homepage. All companies also provided information about applicable laws/regulations. Most companies provided details about data storage/processing and about half describe data retention policy. Few provided details about how AI systems work, how AI systems link to company practices, or risks of AI use. Most companies provided some form of information about AI or machine learning on a site away from the privacy policy. To ensure the auditability and accountability of AI systems, companies are encouraged to improve upon these transparency efforts by better-aligning with the calls for AI transparency in The White House Blueprint for an AI Bill of Rights. Accessible and plain language notices are recommended, as is the inclusion of information about how AI systems work at each company, and the associated implications and risks of automated decision-making that may result from digital service use.