Calls for Papers

Hosted & sponsored by:

• Sorbonne University Abu Dhabi (Organizer)

• HEG-Geneva School of Business Administration, Switzerland (Co-Organizer)

• National Library and Archives (UAE) – Host & Sponsor of Day 3 (11 November 2026)

Dates: 9th - 11th November, 2026

Location: Online and Onsite at Sorbonne University Abu Dhabi, Abu Dhabi, UAE

Registration Fee: Free of charge for all presenters and attendees

About the Conference

The integration of Large Language Models (LLMs) within the public sector is anticipated to deliver significant improvements in service delivery, operational efficiency, and the overall responsiveness and transparency of public institutions. This advancement may also reflect a governmental commitment to modernization and the adoption of innovative methodologies. LLMs are equipped to automate routine functions such as data entry, report generation, and customer service inquiries, thereby allowing government personnel to focus on more complex and strategic responsibilities. Additionally, LLMs exhibit remarkable capabilities in processing and analyzing large volumes of unstructured data, including public feedback and interactions on social media platforms. This proficiency enables governmental agencies to extract insights, identify trends, and engage in informed, data-driven decision-making. The multilingual capabilities of LLMs further enhance accessibility for diverse populations, thereby bridging communication gaps and ensuring equitable access to information for all citizens. Furthermore, LLMs play a crucial role in optimizing the policy-making process by assisting in the drafting of policy documents, summarizing research findings, and evaluating the potential impacts of proposed regulations, thereby elevating the quality of governance. When implemented judiciously, LLMs can enhance transparency in governmental operations by providing clear explanations of policies and decisions, which in turn fosters trust among citizens.

Nonetheless, the implementation of LLMs in the public sector is accompanied by a range of challenges. Organizations face technical complexities, including the need for substantial computational resources and specialized expertise. Moreover, the outputs generated by LLMs may lack reliability, raising significant concerns. Key issues include the management of sensitive data, which poses critical challenges related to privacy, security, and compliance with regulations such as the General Data Protection Regulation (GDPR). Additionally, the potential for bias in the outputs produced by LLMs presents significant risks of discrimination within decision-making processes across various public services. The complexities surrounding transparency and accountability are further complicated by the "black box" nature of LLMs, which can hinder trust and understanding among stakeholders. Consequently, the intelligent automation of public sector operations necessitates the establishment of robust data governance frameworks, sophisticated hybrid architectures, and comprehensive compliance mechanisms. The public sector is regulated by a comprehensive legal framework, which necessitates that its operations are not only efficient but also aligned with fundamental democratic principles such as transparency and accountability. These principles are essential for sustaining public trust. The purpose of this conference is to examine the developments associated with the implementation of Large Language Models (LLMs) within this context.

This conference, therefore, aims to facilitate the engagement of participants across a diverse array of disciplines. We therefore invite contributions from researchers, policymakers, public sector LLM implementers, information professionals, public sector officials and administrators, data governance and compliance officers, knowledge managers, sustainability strategists, technology and IT specialists, ethics committees, and civil society organizations concerned with transparency, equity, and the implications of LLMs on citizen rights and access to information. Contributions may encompass empirical research, theoretical insights, methodological developments, case studies, or reflective practice papers.

Objectives

• To explore the maintenance of democratic principles, transparency, and accountability within public sector operations amidst LLM deployment.

• To assess the impact of large language models (LLMs) on public sector practices and their alignment with established regulatory standards.

• To establish the impact the implementation of LLMs has on the management of government information in processes where they have been implemented.

• To identify best practices for leveraging LLMs while ensuring adherence to the principles of democracy, transparency, and accountability in public sector initiatives.

• To identify challenges emanating from the implementation of LLMs in the public sector operations.

• To establish when government officials are being given the required skills to engage with the new developments in the public sector in the realm of LLMs.

• To establish the social, economic and ecological implications of the use of LLMs in the public sector. 

Topics

• Advancements in Public Sector Efficiency

• Ethical Governance and Data Management

• Transparency and Trust in Public Sector Governance

• Technical Challenges and Resource Requirements for LLM Implementation

• Multilingual Accessibility and Inclusivity

• Ethical and Governance Frameworks for AI in Records and Information Management

• Sustainability of LLMs in the Public Sector

• LLMs in Knowledge and Information Retrieval

• Capacity Building and Professional Adaptation in the AI Era

• Case Studies and Evaluation of LLM Implementation in the Public Sector

• Social, Ecological and Economic Implications of LLMs in the Public Sector

• LLMs and cultural heritage management. 

Submission Deadlines

• Extended abstract submission deadline: 31st July 2026

• Notification of acceptance: 14th August 2026

• Full paper submission: 30th September 2026 

Publication Opportunity 

Selected papers will be published in an edited book indexed in Scopus/Web of Science.

Abstract Submission

Click here to submit your abstract.