The rapid advancement and adoption of Large Language Models (LLMs) represent a paradigm shift with profound implications for all sectors, particularly public administration and governance. Government agencies manage massive volumes of data, policy documents, and public inquiries, making them prime candidates for the efficiency gains promised by generative AI. From automated document summarization and legal compliance to enhancing citizen service chatbots, LLMs offer unprecedented opportunities to modernize public service and improve evidence-based decision-making.

However, the integration of LLMs into the government sector introduces unique and critical challenges. Public sector use cases carry a heightened risk profile because they can affect fundamental rights, national security, and public trust. Key barriers include model hallucination, data privacy for sensitive citizen information, algorithmic bias, and the need for robust explainability and accountability.

The GLARE workshop provides a specialized venue for peer review and publication of high-impact research at the intersection of governance, large language models, applications, risks, and ethics. The workshop is dedicated to curating research that establishes public-service-tailored evaluation benchmarks and rigorous frameworks for ethical and legal governance of LLMs.

Topics of Interest:

We invite original research, empirical case studies, and position papers addressing the following themes:

1. AI / Agentic AI Applications & Use Cases

Citizen-centric services: advanced multi-modal interfaces, automated information retrieval, navigation systems, and related applications.
Internal administration: intelligent document management, knowledge discovery, automated report summarization, and synthesis.
National security: intelligence analysis and automated threat assessment.
Empirical evidence: case studies on AI deployments in public administration.

2. Trust, Risk, & Security

Factual integrity: mitigating hallucination to ensure accuracy in high-stakes contexts.
Data sovereignty: security architectures for classified or sensitive data.
System robustness: defending against adversarial attacks and prompt injections.
Validation: red-teaming methods and rigorous safety evaluations.

3. Ethics & Legal Governance
Regulatory alignment: AI governance frameworks and emerging public sector mandates.
Algorithmic fairness: audit and mitigation strategies for bias in public service delivery.
Explainable AI: transparency in government decision-support systems.
Accountability: liability and responsibility for AI-mediated outcomes in public administration.

4. Design & Infrastructure
Domain specialization: fine-tuning Gov-LLMs on restricted or sovereign datasets.
Hybrid architectures: balancing on-premise and cloud deployments for security.
Evaluation standards: specialized benchmarks and performance metrics for government.
Sustainability: cost-efficient scaling and computational resource management.

Review and Publication:

All submitted papers will undergo a rigorous peer review process by at least two expert reviewers from the Technical Program Committee.

Acceptance decisions will be based on technical quality, novelty, and relevance to the workshop scope.

Accepted papers will be submitted for inclusion in IEEE Xplore, following the standard AIBThings-2026 procedure, subject to meeting IEEE Xplore's scope and quality requirements.