International Deliberation on Long Covid (debategraph.org)

DebateGraph is a collaborative, cloud-based platform designed to help users explore and map complex issues through dynamic, continuously updated deliberation graphs. The platform enables the interrelation of salient dimensions, such as evidence, arguments, and policy responses, and supports annotations with text, videos, and images. Within AI4Deliberation, DebateGraph will be enhanced with AI features to support an international deliberation on the societal impacts of Long Covid, which has affected over 65 million people globally.

Many individuals living with Long Covid, along with their families, feel neglected and have begun organizing active online communities to foster societal understanding and accelerate effective policy responses. This pilot will develop a deliberation graph for Long Covid, combining human curation and AI augmentation, and involving a wide range of stakeholders such as patient groups, scientists, health professionals, and policymakers.

Pilot Activities:

Developing the Deliberation Graph:
The graph will dynamically include:

Evidence underlying Long Covid’s characteristics.

Personal, societal, and economic implications.

Potential policy responses and associated arguments.

Experimenting with AI-Augmented Human Curation:
A positive feedback loop will be tested, where:

Human curation enhances the signal-to-noise ratio from AI’s analysis of relevant literature.

AI tools, in turn, accelerate the human curation process of the graph.

Goals:

Dynamic Knowledge Graph:
Create an open deliberation graph as a shared public good for Long Covid, accessible globally.

Enhanced Collaboration:
Support effective collaboration between global stakeholders using AI to improve deliberation quality and efficiency.

National-Level Climate Change Adaptation Deliberation (ActionAid, Italy)

This pilot focuses on implementing a national-scale deliberative process centered on the National Climate Change Adaptation Plan (PNACC), approved by the Italian Government in January 2024. The PNACC serves as a strategic framework for planning and executing climate adaptation actions. The deliberative process will analyze specific elements of the plan and its application, such as related environmental policies, and generate actionable recommendations.

The deliberative path consists of three main phases:

1. Information & Learning: Providing participants with the necessary knowledge to engage effectively.
2. Deliberation: Facilitating discussions to explore diverse perspectives and build consensus.
3. Proposal Definition: Developing concrete recommendations based on the deliberations.

Background and AI4Deliberation Integration:

In 2019, a similar deliberative process was conducted by AAIT, focused on Disaster Risk Management. This process primarily utilized physical channels, such as workshops, to engage citizens and experts, resulting in policy recommendations. A website (#Sicuriperdavvero) complemented this effort by promoting discussion content and allowing for feedback mechanisms, including comment submission and voting. Comments were moderated before publication to ensure quality and traceability.

Under AI4Deliberation, this pilot aims to explore the potential of integrating AI tools into a digital deliberative platform, transitioning from the physical deliberation model to an online format. Key innovations include:

Supporting text-based deliberations with video discussions and additional media formats.

Utilizing the Common Grounds platform, owned by BRANE, as the foundation for online deliberations.

Leveraging AI to enhance inclusivity and foster broader participation in the deliberation process.

Goals:

AI Integration: Experiment with AI tools to facilitate discussions, analyze feedback, and generate insights.

Inclusive Deliberation: Use digital platforms to involve a wider range of participants, breaking geographical and logistical barriers.

Innovative Pathways: Develop and test an online deliberative framework as a complement to traditional physical models.

By integrating AI and digital platforms, the Italian pilot seeks to transform climate adaptation deliberations, promoting inclusivity, efficiency, and actionable outcomes.

Local Deliberation at Bamberg Smart City, Germany

This pilot in Bamberg, Germany, focuses on testing and evaluating the integration of AI tools within two distinct citizen participation models: top-down and bottom-up approaches. These models aim to enhance municipal dialogue, citizen engagement, and community-driven actions.

Case 1: Top-Down Citizen Participation (Bamberg.gestalten.de)

The Bamberg.gestalten.de platform currently operates under a top-down approach to digital citizen participation. On this Consul-based platform, the municipality introduces topics to gather input from residents. To ensure participation authenticity, user data is cross-checked with the resident registration directory, preventing bots or fake accounts.

Examples of issues addressed on the platform include:

Redesigning and repurposing public spaces.

Drafting a municipal data policy.

Within AI4Deliberation, the platform will incorporate and evaluate various AI features, such as:

Fact-checking: Ensuring accuracy and reliability in discussions.

Mitigating misinformation and disinformation: Testing AI’s ability to create an evidence-based foundation for municipal dialogue.

Case 2: Bottom-Up Citizen Participation

A new engagement platform is under development as part of another national project. Unlike the top-down approach, this platform adopts a bottom-up model, enabling citizens to:

Freely present and discuss their concerns within the community and with local administration.

Take proactive measures to address their pain points through enabling functions incorporated into the platform.

Within AI4Deliberation, the following aspects will be tested:

AI’s impact on engagement: Assess whether AI applications can make local engagement platforms more attractive and effective.

Bridging the digital divide: Explore AI’s potential to enhance participation in small-scale networks by making digital platforms more accessible and functional.

Since this platform is still under development, a collaborative approach between the two projects is anticipated, with AI4Deliberation results feeding directly into the platform’s evolution.