LIST OF PANELS

Remarkable progress and access to digital technology has led to substantial increase in creation, distribution and consumption of digital information (Jaidka et al., 2024; Ng & Taeihagh, 2021). Consumers now have curated and personalized access to information and opinions. The transformation in digital information ecosystem, while has democratised information, it has also enabled unchecked growth of unverifiable, false, deceitful, and at times, harmful information. Classified as misinformation, disinformation, and mal-information (MDM), its proliferation has led to a ‘digital information paradox’- a situation of unbridled access to information accompanied by low levels of trust in such information (Jaidka et al., 2024). Beyond trust in information, proliferation of MDM been associated with substantial public harm arising from decline in social trust and trust in political and administrative institutions (Saurwein & Spencer-Smith, 2020). Recent cases where MDM were extensively used to influence electoral and democratic outcomes have manifested such concerns in real time and increased urgency amongst governments to address concerns related to MDM. The emergence of Generative Artificial Intelligence (GenAI) has further exacerbated these issues (Khanal et al., 2024). The ability of GenAI to mass-produce synthetic digital information that mimics the features of human-generated content has raised concerns about exacerbation of MDM and its impact on building a digitally resilient society (Jaidka et al., 2024).

While there has been a widespread acknowledgement about the issue of MDM, there are significant divergences in problem definition. An emerging body of literature focuses on MDM as a technical problem and focuses on identifying issues related to information consumption and information lifecycle (Jaidka et al., 2024). It seeks to identify processes and mechanisms associated within digital ecosystems that incentivize and facilitate growth of MDM and identify processes to design robust information systems (Abbas & Taeihagh, 2024; Ng & Taeihagh, 2021). A more common set of studies approach this issue politically, focusing on specific micro-, meso-, and macro-level drivers of creation, distribution and consumption of MDM. Studies here focus on relationship between issues such as income, political orientation, voting behavior, and changing geopolitical and socio-economic landscapes and consequences of MDM (Corner, 2017; Farkas & Schou, 2018; Lazer et al., 2018; Pennycook & Rand, 2021). Finally, the last set of studies considers MDM to be a crucial element of a larger socio-technical assemblage on information. MDM’s growth is situated within an ecosystem of declining trust in existing political systems, misaligned incentives of information creation that encourages clickbait, rise in digital platforms, and crisis of traditional journalism (Cassar, 2023).

Differences in approaches to problem definition has also led to divergence in arguments for governance approaches. Significant differences can be noticed in terms of urgency and comprehensiveness of the problem, in governance of information, and in extent and nature of participation of different actors involved in the digital ecosystem (Cassar, 2023; Jaidka et al., 2024; Kozyreva et al., 2024; Ulnicane, 2024). There is also a recognition that extensive control on information can come at the cost of fundamental democratic rights such as freedom of information (Kozyreva et al., 2023; Neo, 2021). This panel will explore some of these challenges and approaches taken to build trust in digital information. Papers in the panel will discuss the potential and existing regulatory and policy approaches available to the policymakers to address the governance challenges and build trust in digital information systems. More specifically, the papers in the panel will answer the following research questions:

• What are the technical, social, ethical, and governance challenges associated with digital information in the age of GenAI?
• What are the existing policy and regulatory frameworks adopted by governments to combat the challenges brought on by MDM?
• How can governments build trust in the stages of digital information lifecycle?
• How can trust in digital information contribute to building a digitally resilient society?

This panel welcomes conceptual and empirical papers from established and new researchers exploring the role of policy design to govern digital information and build trust given the challenges presented by MDM in the era of GenAI.

·         Theoretical and conceptual papers on the governance of digital information

·         Studies on the policy and governance challenges presented by MDM on the trust in digital information

·         Theoretical, conceptual, and empirical papers on the role and impact of generative AI on the digital information lifecycle

·         Case studies and empirical papers on the current policy and regulatory approaches to managing the challenges presented by MDM

References

Abbas, F., & Taeihagh, A. (2024). Unmasking deepfakes: A systematic review of deepfake detection and generation techniques using artificial intelligence. Expert Systems With Applications, 124260.

Cassar, D. (2023). The Misinformation Threat: A Techno-Governance Approach for Curbing the Fake News of Tomorrow. Digital Government: Research and Practice, 4(4), 1–28. https://doi.org/10.1145/3631615

Corner, J. (2017). Fake news, post-truth and media–political change. Media, Culture & Society, 39(7), 1100–1107. https://doi.org/10.1177/0163443717726743

Farkas, J., & Schou, J. (2018). Fake News as a Floating Signifier: Hegemony, Antagonism and the Politics of Falsehood. Javnost - The Public, 25(3), 298–314. https://doi.org/10.1080/13183222.2018.1463047

Jaidka, K., Chen, T., Chesterman, S., Hsu, W., Kan, M.-Y., Kankanhalli, M., Lee, M. L., Seres, G., Sim, T., Taeihagh, A., Tung, A., Xiao, X., & Yue, A. (2024). Misinformation, Disinformation, and Generative AI: Implications for Perception and Policy. Digital Government: Research and Practice, 3689372. https://doi.org/10.1145/3689372

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. https://doi.org/10.1093/polsoc/puae012

Kozyreva, A., Herzog, S. M., Lewandowsky, S., Hertwig, R., Lorenz-Spreen, P., Leiser, M., & Reifler, J. (2023). Resolving content moderation dilemmas between free speech and harmful misinformation. Proceedings of the National Academy of Sciences, 120(7), e2210666120. https://doi.org/10.1073/pnas.2210666120

Kozyreva, A., Lorenz-Spreen, P., Herzog, S. M., Ecker, U. K. H., Lewandowsky, S., Hertwig, R., Ali, A., Bak-Coleman, J., Barzilai, S., Basol, M., Berinsky, A. J., Betsch, C., Cook, J., Fazio, L. K., Geers, M., Guess, A. M., Huang, H., Larreguy, H., Maertens, R., … Wineburg, S. (2024). Toolbox of individual-level interventions against online misinformation. Nature Human Behaviour, 8(6), 1044–1052. https://doi.org/10.1038/s41562-024-01881-0

Lazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., Schudson, M., Sloman, S. A., Sunstein, C. R., Thorson, E. A., Watts, D. J., & Zittrain, J. L. (2018). The science of fake news. Science, 359(6380), 1094–1096. https://doi.org/10.1126/science.aao2998

Neo, R. (2021). The International Discourses and Governance of Fake News. Global Policy, 12(2), 214–228. https://doi.org/10.1111/1758-5899.12958

Ng, L. H. X., & Taeihagh, A. (2021). How does fake news spread? Understanding pathways of disinformation spread through APIs. Policy & Internet, poi3.268. https://doi.org/10.1002/poi3.268

Pennycook, G., & Rand, D. G. (2021). The psychology of fake news. Trends in Cognitive Sciences, 25(5), 388–402.

Saurwein, F., & Spencer-Smith, C. (2020). Combating Disinformation on Social Media: Multilevel Governance and Distributed Accountability in Europe. Digital Journalism, 8(6), 820–841. https://doi.org/10.1080/21670811.2020.1765401

Ulnicane, I. (2024). Governance fix? Power and politics in controversies about governing generative AI. Policy and Society, puae022. https://doi.org/10.1093/polsoc/puae022