CALL FOR PAPERS

AI & Open Government Workshop (AIOG)

Co-located with the 21st International Conference on
Artificial Intelligence and Law (ICAIL 2026)

Singapore, June 8, 2026

https://aiog.net

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OVERVIEW

The AI & Open Government workshop (AIOG) focuses on how modern AI
tools and techniques, including Large Language Models (LLMs), can
support government accountability and transparency by improving
public access to government records and enabling more reliable and
compliant disclosure processes.

In many jurisdictions, open government and access to information laws
such as Freedom of Information (FOIA) and Access to Information (ATI)
require large-scale public disclosure of government records, resulting
in massive, multimodal data collections whose complexity increasingly
challenges both legal compliance and technical processing. At the same
time, governments face strict legal obligations to disclose information
within statutory deadlines, while protecting sensitive and personal
information.

The workshop addresses two key perspectives:
* AI for citizens: Tools and techniques for improving search,
exploration, and understanding of public government information
* AI for governments: Assisting in accessibility, pre-processing,
metadata enrichment, retrieval, filtering, and protecting
sensitive information consistent with public disclosure laws


TOPICS OF INTEREST

Topics include, but are not limited to:
* AI-augmented search, retrieval, and summarization for public
records
* Technology-assisted review for FOIA and records access requests
* Automated sensitivity review and redaction under FOIA/GDPR
* Metadata enrichment and entity extraction for government record
discovery
* Multimodal processing of scans, PDFs, and legacy document formats
in public archives
* Agentic AI for FOIA request triage and handling
* Public-facing tools for navigating heterogeneous government data
repositories
* Formalising legal standards for disclosure, exemptions, and harm
in AI-assisted access workflows
* Governance, auditability, and explainability of AI-assisted
disclosure, including human-in-the-loop review
* Automated classification for government records retention
* Retrieval Augmented Generation (RAG) architectures and generative
AI for public government records and cultural heritage archives
* AI-assisted declassification of government records for public
release


SUBMISSION GUIDELINES

We invite submissions of:
* Research papers (3-9 pages + references): original research
contributions
* Position papers (up to 5 pages + references): insights from
practice

Papers must be formatted using the ACM sigconf template (for LaTeX)
or the interim template layout.docx (for Word), both available at:
http://www.acm.org/publications/proceedings-template

Papers should be submitted via the submission portal at:
https://submit.aiog.net

Reviewing will be double-blind, i.e., papers submitted for review
must not include names and affiliations of the authors. Accepted
papers will be published in OpenReview proceedings.

Authors of accepted papers will be invited to present their work
in person in Singapore.


IMPORTANT DATES

Paper submission deadline: April 9, 2026
Notification of acceptance: May 1, 2026
Camera-ready deadline: May 20, 2026
Workshop date: June 8, 2026

All deadlines are 23:59 AoE (Anywhere on Earth) time.


TARGET AUDIENCE

This workshop is relevant to:
* The open government community
* FOIA requesters, including investigative journalists and civil
society organisations
* The legal community interested in public records discovery and
disclosure obligations
* Privacy and data protection advocates, particularly those focused
on GDPR-aligned sensitivity review
* Government agencies and archival institutions working on
information management and disclosure workflows
* AI researchers working on information retrieval and natural
language processing
* The intelligence and declassification community


WORKSHOP ORGANIZERS

David Graus
University of Amsterdam
d.p.graus@uva.nl

Graham McDonald
University of Glasgow
graham.mcdonald@glasgow.ac.uk

Jason R. Baron
University of Maryland
jrbaron@umd.edu