LIST OF PANELS

Objectives

 As artificial intelligence (AI) becomes increasingly integral to various sectors of the global economy, the imperative to establish effective governance and regulatory frameworks has never been more pressing. Governments around the world are striving to navigate the complex landscape of AI regulation, balancing the promotion of innovation with the protection from potential risks while maintaining ethical integrity. This panel aims to provide a comprehensive analysis of diverse approaches to AI governance and regulation, with a focus on strategies adopted by emerging economies.

Research Question

How do emerging economies navigate the balance between innovation and regulation in AI governance, and what role do regulatory frameworks from major jurisdictions (EU, US, China) play in shaping their approaches?

Literature Review

Recent scholarship has revealed a complex landscape of AI governance dominated by three distinct regulatory approaches. These approaches reflect fundamentally different philosophies: China's state-driven development model, the US's market-driven approach, and the EU's rights-driven framework (Bradford, 2023). This panel is interested in how these jurisdictions compete not only for technological dominance but also for regulatory supremacy, seeking to project their models globally (Smuha, 2021).

The concept of regulatory influence is particularly evident in what has been termed the "Brussels Effect," whereby the EU's market power enables it to set de facto global standards (Bradford, 2012, 2020). In the realm of digital governance, this phenomenon is exemplified by the GDPR's impact on global data protection standards (Mahieu et al., 2021). However, AI's rapid evolution and geopolitical significance complicate the establishment of uniform global standards (Almada & Radu, 2024). Parallel to this, research identifies an emerging "Beijing Effect," where China's regulatory framework, emphasizing pro-growth and developmental priorities, particularly influences emerging economies (Erie & Streinz, 2021; Migliorini, 2024).

This panel aims to address a critical gap in current discussions of AI governance by bringing together experts studying how emerging economies develop hybrid regulatory models(Chan et al., 2024; Migliorini & Neuwirth, 2023; Papyshev & Yarime, 2023). While most academic discussions on AI governance focus on the EU, US, and China’s approaches, our panel will be especially interested in examining how other countries navigate between these established frameworks while developing context-specific approaches.

Scientific Relevance

This panel will adopt a comparative analysis approach, examining case studies from various emerging economies to understand their unique regulatory challenges and opportunities. This panel will contribute to the academic discussions by shedding light on the under-explored area of AI regulation in emerging economies. It aims to generate comprehensive insights into the dynamic interplay between national priorities and international standards, ultimately informing policymakers and stakeholders engaged in crafting effective AI governance frameworks.

Through interdisciplinary dialogue, this panel aspires to enhance our understanding of the multifaceted landscape of AI governance and its implications for global cooperation and innovation.

We invite scholars, policymakers, and practitioners to submit papers that explore the diverse approaches to AI governance and regulation from various perspectives. Submissions may focus on a single jurisdiction, offer comparative analyses across multiple countries, or examine the impact of international initiatives on national frameworks. We welcome a broad range of methodologies, including legal analysis, qualitative studies, computational social science approaches, and economics-based research. Contributions should aim to uncover the unique challenges and opportunities different jurisdictions face in developing their AI regulatory frameworks, with particular interest in strategies adopted by emerging economies and the influence of international frameworks on national governance strategies.

References

Almada, M., & Radu, A. (2024). The Brussels Side-Effect: How the AI Act Can Reduce the Global Reach of EU Policy. German Law Journal, 1–18. https://doi.org/10.1017/glj.2023.108

Bradford, A. (2012). The Brussels Effect. Northwestern University Law Review, 107, 1.

Bradford, A. (2020). The Brussels Effect: How the European Union Rules the World. Oxford University Press.

Bradford, A. (2023). Digital Empires: The Global Battle to Regulate Technology. Oxford University Press.

Chan, K. J. D., Papyshev, G., & Yarime, M. (2024). Balancing the tradeoff between regulation and innovation for artificial intelligence: An analysis of top-down command and control and bottom-up self-regulatory approaches. Technology in Society, 79, 102747. https://doi.org/10.1016/j.techsoc.2024.102747

Erie, M. S., & Streinz, T. (2021). The Beijing Effect: China’s “Digital Silk Road” as Transnational Data Governance (SSRN Scholarly Paper ID 3810256). Social Science Research Network. https://papers.ssrn.com/abstract=3810256

Mahieu, R., Asghari, H., Parsons, C., van Hoboken, J., Crete-Nishihata, M., Hilts, A., & Anstis, S. (2021). Measuring the Brussels Effect through Access Requests: Has the European General Data Protection Regulation Influenced the Data Protection Rights of Canadian Citizens? Journal of Information Policy, 11, 301–349. https://doi.org/10.5325/jinfopoli.11.2021.0301

Migliorini, S. (2024). China’s Interim Measures on generative AI: Origin, content and significance. Computer Law & Security Review, 53, 105985. https://doi.org/10.1016/j.clsr.2024.105985

Migliorini, S., & Neuwirth, R. J. (2023). The relevance of culture in regulating AI and big data: The experience of the Macao SAR. In M. Findlay, L. M. Ong, & W. Zhang (Eds.), Elgar Companion to Regulating AI and Big Data in Emerging Economies (pp. 138–157). Edward Elgar Publishing. https://doi.org/10.4337/9781785362408.00015

Papyshev, G., & Yarime, M. (2023). The challenges of industry self-regulation of AI in emerging economies: Implications of the case of Russia for public policy and institutional development. In Elgar Companion to Regulating AI and Big Data in Emerging Economies (pp. 81–98). Edward Elgar Publishing. https://www.elgaronline.com/edcollchap/book/9781785362408/chapter4.xml

Smuha, N. A. (2021). From a ‘race to AI’ to a ‘race to AI regulation’: Regulatory competition for artificial intelligence. Law, Innovation and Technology, 13(1), 57–84. https://doi.org/10.1080/17579961.2021.189830