University of Kwazulu-Natal

Turning Ai Into A Regulatory Sandbox Exploring Information Disorder

Bonisiwe Shabane
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turning ai into a regulatory sandbox exploring information disorder

Information disorder, understood as the creation, dissemination, and amplification of false, misleading, or harmful information, has raised growing concern in recent years. The convergence of multiple interconnected factors—planetary spread of online information, network interactions that amplify cognitive biases, and algorithms fostering echo chambers—has made the phenomenon difficult to predict and challenging for public authorities to counteract. In such a scenario, a major obstacle to developing effective regulatory and control policies lies in the difficulty of grounding decisions in sufficiently realistic models and representations accounting for the many mechanisms influencing the... Actually, research in computational social science has made huge strides in illuminating the core dynamics of information disorder, primarily through mathematical models drawing from graph theory, complex network analysis, and operations research. Despite their strengths, such approaches rely on abstract representations. Such representations often struggle to capture the complexity of the reality which is shaped by emotional, psychological, and adaptive dynamics.

Therefore, they are hard to integrate into purely mathematical descriptions. This paper presents ongoing research aimed at overcoming such limitations through a hybrid strategy for the in silico exploration of mitigation strategies. We integrate two components: (1) an agent-based simulation module to reproduce the core dynamics of information disorder in a controlled environment, leveraging well-established theoretical models; (2) a deep learning module in charge of piloting... This approach allows for a gradual increase in model complexity, leveraging machine learning to identify the most effective solutions. Suitable to being extended to other social issues, such an approach allows for a gradual increase in model complexity, enabling the creation of progressively more realistic models while entrusting the machine learning component with... This is a preview of subscription content, log in via an institution to check access.

Price excludes VAT (USA) Tax calculation will be finalised during checkout. All data analysed and code used during this study are included in the GitHub repository https://shorturl.at/oOKzm. https://www.eui.eu/news-hub?id=echo-chambers-in-social-networks-how-to-eliminate-the-spread-of-misinformation. Yes, regulatory sandboxes can be a good idea. These controlled test beds for new technologies are moving to Washington, with Sen. Ted Cruz introducing a bill to establish federal AI sandboxes.

Framed as exceptions from burdensome regulation, the proposal mirrors what has been done in the U.K. and Europe. Artificial intelligence continues to race ahead of existing governance models, raising concerns about safety, security and global competitiveness. Policymakers are scrambling to find tools that protect consumers without slowing innovation. Among these proposals is the introduction of regulatory sandboxes, controlled environments where companies can test new technologies under oversight but with temporary flexibility from certain rules. Sen.

Ted Cruz, chair of the Senate Commerce Committee, unveiled a bill to establish federal AI sandboxes. The initiative comes as dozens of countries experiment with sandboxes in finance, healthcare and now AI. The European Union AI Act, for instance, requires member states to set up AI sandboxes, and the United Kingdom pioneered this model in financial services nearly a decade ago. The evidence suggests this approach can work if designed with transparency, enforcement, and public safeguards in mind. Regulatory sandboxes promote innovation and foster learning. Yet they also risk regulatory capture and can distort the competitive environment by advantaging sandbox participants.

A regulatory sandbox is a structure in which innovators can test technologies under the watch of regulators without immediately facing the full weight of compliance. Borrowed from software development, the term has evolved into a legal and policy tool that allows experimentation while limiting risk. The World Economic Forum reported that AI-generated misinformation and disinformation are the second most likely threat to present a material crisis on a global scale in 2024 at 53% (Sept. 2023). Artificial intelligence is automating the creation of fake news at a rate disproportionate to its fact-checking. It is spurring an explosion of web content mimicking factual articles that instead disseminate false information about grave themes such as elections, wars and natural disasters.

According to a report by the Centre for the Study of Democratic Institutions, a Canadian think tank, the most prevalent effect of Generative AI is the ability to flood the information ecosystem with misleading... As reported by Democracy Reporting International during the 2024 elections of the European Union, Google's Gemini, OpenAI’s ChatGPT 3.5 and 4.0, and Microsoft’s AI interface ‘CoPilot’ were inaccurate one-third of the time when engaged... Therefore, a need for an innovative regulatory approach like regulatory sandboxes which can address these challenges while encouraging responsible AI innovation is desired. False or misleading information created, amplified, or spread using artificial intelligence technologies is AI-driven misinformation. Machine learning models are leveraged to automate and scale the creation of false and deceptive content. Some examples are deep fakes, AI-generated news articles, and bots that amplify false narratives on social media.

The biggest challenge is in the detection and management of AI-driven misinformation. It is difficult to distinguish AI-generated content from authentic content, especially as these technologies advance rapidly. AI-driven misinformation can influence elections, public health, and social stability by spreading false or misleading information. While public adoption of the technology has undoubtedly been rapid, it is yet to achieve true acceptance and actually fulfill its potential in a positive manner because there is widespread cynicism about the technology... The general public sentiment about AI is laced with concern and doubt regarding the technology’s trustworthiness, mainly due to the absence of a regulatory framework maturing on par with the technological development. Thanks to Marlene Smith for her research contributions.

As policymakers worldwide seek to support beneficial uses of artificial intelligence (AI), many are exploring the concept of “regulatory sandboxes.” Broadly speaking, regulatory sandboxes are legal oversight frameworks that offer participating organizations the opportunity... Sandboxes often encourage organizations to use real-world data in novel ways, with companies and regulators learning how new data practices are aligned – or misaligned – with existing governance frameworks. The lessons learned can inform future data practices and potential regulatory revisions. In recent years, regulatory sandboxes have gained traction, in part due to a requirement under the EU AI Act that regulators in the European Union adopt national sandboxes for AI. Jurisdictions across the world, such as Brazil, France, Kenya, Singapore, and the United States (Utah) have introduced AI-focused regulatory sandboxes, offering current, real-life lessons for the role they can play in supporting beneficial use... More recently, in July 2025, the United States’ AI Action Plan recommended that federal agencies in the U.S.

establish regulatory sandboxes or “AI Centers of Excellence” for organizations to “rapidly deploy and test AI tools while committing to open sharing of data and results.” As AI systems grow more advanced and widespread, their complexity poses significant challenges for legal compliance and effective oversight. Regulatory sandboxes can potentially address these challenges. The probabilistic nature of advanced AI systems, especially generative AI, can make AI outputs less certain, and legal compliance therefore less predictable. Simultaneously, the rapid global expansion of AI technologies and the desire to “scale up” AI use within organizations has outpaced the development of traditional legal frameworks. Finally, the global regulatory landscape is increasingly fragmented, which can cause significant compliance burdens for organizations.

Depending on how they are structured and implemented, regulatory sandboxes can address or mitigate some of these issues by providing a controlled and flexible environment for AI testing and experimentation, under the guidance and... This framework can help ensure responsible development, reduce legal uncertainty, and inform more adaptive and forward-looking AI regulations. 1. Key Characteristics of a Regulatory Sandbox Explore how global AI regulatory sandboxes are testing new AI systems while balancing innovation, ethics, and compliance. How do you balance innovation with responsibility?

Around the world, AI regulatory sandboxes are emerging as test beds for this delicate balance—where cutting-edge AI technologies can be trialed under watchful eyes. These sandboxes, often backed by governments and regulators, are becoming crucial tools for shaping the ethical use of AI. But can they keep pace with the breakneck speed of AI evolution? AI regulatory sandboxes are controlled environments where companies can experiment with new AI technologies while working closely with regulators. Unlike traditional product testing, sandboxes provide real-world testing without the full weight of permanent regulations. For instance, the UK’s Financial Conduct Authority (FCA) has long championed sandboxes for fintech, and its AI-specific initiatives are seen as models for other nations.

Similarly, Singapore’s AI sandbox allows companies to validate compliance with its Model AI Governance Framework.

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