LIST OF PANELS

The governance of artificial intelligence (AI) is witnessing rapid developments with the passage of the EU-AI Act, the world’s first comprehensive law on AI. As a disruptive technology, AI has the potential to solve global challenges like climate change, improve efficiency in various sectors like manufacturing, health, education, and transportation. However, there are socio-technical, ethical, and safety challenges of AI adoption that have been discussed extensively (Agrawal et al., 2019; Burton et al., 2020; Calo, 2017; Etzioni & Etzioni, 2017; Ruschemeier, 2023; Sood & Lim, 2023; Tan & Taeihagh, 2021). The proliferation of generative AI along with general and narrow AI has led to distinctive and unique challenges to this landscape. 

In this backdrop, governance of AI has received considerable attention with governments following varied approaches ranging from self-regulation and market-based approaches and a mix of entrepreneurial and regulatory governance (Djeffal et al., 2022). For instance, while the EU follows a horizontal approach without differentiating sectors for harmonization, the US adopts a sectoral approach (Haie et al., n.d.). The EU-AI Act delineates AI systems on the basis of their risks—unacceptable, high, limited, and minimal risk, while the UK government classifies risks of AI as societal harms, misuse risks, and autonomy risks (ibid.). Such differences will have ramifications for global governance of AI and the impact of the actions of different actors like the big tech (e.g. OpenAI, Meta, Google, Microsoft) involved in developing and integrating AI technologies in products and services (Khanal et al. 2024). With the pace of innovations of such companies moving faster than legislations and policies, insights into the governance of AI systems to balance innovation and regulation will be imperative for policymaking.

The panel will explore facets related to regulation of AI focusing on the following (not limited to) questions:  

·         What are the different frameworks used to understand the risks and formulate governance mechanisms for AI?

·         How are governments balancing the innovation across sectors with the risks of AI?

·         To what extent do the current governance mechanisms for AI in the global north and south address the challenges of adoption of AI?

·         What are the challenges associated with implementation of existing AI governance mechanisms in the global north and south?

·         What is the role of policy learning in adoption and governance of AI, especially for middle and low income countries?

The EU-AI Act had laid the foundation for governance and regulation of AI in the world. As this legislation was being deliberated, generative AI has been introduced along with narrow and general AI, making the regulation of AI complex. The use of generative AI has had implications in the education, healthcare, finance, and entertainment sectors(Chen et al., 2023; Mc Kinsey, 2023; Sood & Lim, 2023). In jurisdictions all over the world, especially in the global north, governments are taking steps to overcome the socio-technical challenges of AI by setting up institutes and including actors such as the technology companies.  

The significance of actors such as the technology companies cannot be understated in the regulation of AI since innovation and deployment of AI by them is outpacing the legislations. Several governments have also made strides in proposing guidelines and legislations and examining the evolving rules and processes will be significant for policymaking for AI.

The panel invites papers involving research related to strategies for governing AI, be it narrow or general AI or generative AI. The papers can involve empirical work involving surveys, theoretical work on different conceptualisations of regulation of AI or evolving models focusing on the actors and their strategies towards innovation and regulation of AI.

The papers can focus on the following research questions:

·         Theoretical, conceptual, or empirical studies to understand the regulatory strategies for AI in the global north and south.

·         Investigating the appropriateness and effectiveness of such strategies as well as different facets of regulation and governance of AI with implications for policymaking in different jurisdictions.

·         Analysis of actors involved in the operation and deployment of AI and consequences for the regulation.

·         Single and comparative case studies across different countries, sectors and types of AI to study their governance.

Traditional and systematic literature reviews on governance of AI, conceptual research on regulation of AI, and surveys examining the actors and their actions with implications for policymaking for AI will be suitable for the panel.

REFERENCES

Agrawal, A., Gans, J., & Goldfarb, A. (2019). Economic policy for artificial intelligence. Innovation Policy and the Economy, 19(1), 139–159.

Burton, S., Habli, I., Lawton, T., McDermid, J., Morgan, P., & Porter, Z. (2020). Mind the gaps: Assuring the safety of autonomous systems from an engineering, ethical, and legal perspective. Artificial Intelligence, 279, 103201.

Calo, R. (2017). Artificial Intelligence policy: A primer and roadmap. UCDL Rev., 51, 399.

Chen, B., Wu, Z., & Zhao, R. (2023). From fiction to fact: The growing role of generative AI in business and finance. Journal of Chinese Economic and Business Studies, 21(4), 471–496. https://doi.org/10.1080/14765284.2023.2245279

Djeffal, C., Siewert, M. B., & Wurster, S. (2022). Role of the state and responsibility in governing artificial intelligence: A comparative analysis of AI strategies. Journal of European Public Policy, 29(11), 1799–1821. https://doi.org/10.1080/13501763.2022.2094987

Etzioni, A., & Etzioni, O. (2017). Incorporating ethics into artificial intelligence. The Journal of Ethics, 21(4), 403–418.

Haie, A.-G., Cohen, T., Golodny, A., Shenk, M., Avramidou, M., Goodwin, E., & Arethusa, V. (n.d.). A Comparative Analysis of the EU, US and UK Approaches to AI Regulation. https://www.steptoe.com/en/news-publications/steptechtoe-blog/a-comparative-analysis-of-the-eu-us-and-uk-approaches-to-ai-regulation.html

Khanal, S., Zhang, H., & Taeihagh, A. (2024). Why and how is the power of Big Tech increasing in the policy process? The case of generative AI. Policy and Society, puae012.

Mc Kinsey. (2023, July 10). Generative AI in healthcare: Emerging use for care | McKinsey. https://www.mckinsey.com/industries/healthcare/our-insights/tackling-healthcares-biggest-burdens-with-generative-ai

Ruschemeier, H. (2023). AI as a challenge for legal regulation – the scope of application of the artificial intelligence act proposal. ERA Forum, 23(3), 361–376. https://doi.org/10.1007/s12027-022-00725-6

Sood, A., & Lim, K. (2023, January 7). ChatGPT could change Asian education, but is it a study aid or ‘cheat-bot?’ South China Morning Post. https://www.scmp.com/week-asia/people/article/3205707/chatgpt-personal-tutor-or-cheat-bot-app-could-revolutionise-asias-learning

Tan, S. Y., & Taeihagh, A. (2021). Governing the adoption of robotics and autonomous systems in long-term care in Singapore. Policy and Society, 40(2), 211–231.