[IGF-DAIG] CfP 2026 AI Safety and Security

Thu Feb 12 23:32:44 CET 2026

Dear all,

The DAIG 2026 Call for Papers on  AI Safety and Security has (finally!!!) been published on the IGF website

Please see below and here
https://www.intgovforum.org/en/filedepot_download/288/30344

Feel free to share!
Best
Luca

IGF Coalition on Data and Artificial Intelligence Governance

Call for Papers on
AI Safety and Security

  1.  Background

The IGF Coalition on Data and Artificial Intelligence Governance (DAIG) is a multistakeholder group established under the auspices of the United Nations Internet Governance Forum (IGF). The Coalition fosters dialogue on data and AI governance, encourages analysis of successful and unsuccessful practices, and promotes collaboration to identify solutions that advance sustainable and effective AI governance.

To this end, the DAIG promotes collective studies and multistakeholder interactions to gather evidence, critically analyse regulatory and institutional arrangements, and propose policy updates in AI governance. The Coalition acts as a hub connecting global UN IGF discussions with regional and local initiatives, with a particular focus on debates and experiences from the Global South.

The 2025 Annual Report of the Coalition will focus on AI Safety and Security, highlighting both technical and policy challenges and showcasing experiences from diverse contexts to inform the IGF 2026 discussions.

  1.  Call

The IGF Coalition on Data and Artificial Intelligence Governance (DAIG), under the auspices of the United Nations Internet Governance Forum, invites researchers, policymakers, and practitioners to submit short papers analysing issues related to AI Safety and Security. Submissions may explore theoretical, empirical, technical, or policy-oriented perspectives.
Deadline: 15 May 2026

  1.  Suggested Topics

The topics below are indicative and not exhaustive. Papers may address other relevant dimensions of AI safety and security.
AI Safety

  *   Legal frameworks addressing AI risk mitigation, especially considering sociocultural contexts of the Global Majority
  *   System robustness and reliability: Approaches to ensure consistent AI performance across environments, addressing vulnerabilities due to software bugs, poor input validation, or unstable model behavior.
  *   Specification and assurance: Methods to align AI objectives with human intent, including formal verification, interpretability, and human-in-the-loop mechanisms.
  *   Bias and fairness: Detecting and mitigating bias that can lead to harmful or discriminatory outcomes, such as misclassification in medical or hiring applications.
  *   Regulation of algorithmic bias and discrimination in AI systems impacting marginalized groups
  *   Legal obligations for transparency and user awareness in AI deployment
  *   Safety in autonomous systems: Ensuring reliable perception, decision-making, and control in safety-critical applications (e.g., autonomous vehicles, healthcare robotics).
  *   Safe deployment of large models: Techniques to ensure large language models (LLMs) and foundation models produce accurate, harmless, and unbiased outputs.
  *   Testing, evaluation, and certification: Frameworks for AI safety auditing, benchmarking, and lifecycle assurance..

AI Security

  *   Enforceable requirements for robustness, reliability, and cybersecurity of AI systems, especially in critical infrastructure
  *   Adversarial attacks and defenses: Techniques to detect and mitigate attacks that manipulate model inputs or outputs.
  *   Data poisoning and model corruption: Safeguarding training data against malicious modification that induces harmful or unpredictable behavior.
  *   Model extraction and inversion attacks: Preventing unauthorized access to proprietary model parameters or sensitive training data through inference or querying.
  *   Prompt injection and prompt-based exploits: Defending LLMs and chat-based AI from manipulation to generate harmful, misleading, or confidential outputs.
  *   Supply chain security: Securing datasets, pre-trained models, and software dependencies used in AI system development.
  *   AI-enabled cyberattacks: Understanding and mitigating how AI can be weaponized in phishing, misinformation, and automated hacking.
  *   Security auditing, monitoring, and incident response: Frameworks and best practices for ensuring resilience of AI systems to attacks.
  *   AI security policy and international cooperation: Governance models, risk management frameworks, and global coordination for secure AI deployment.
  *   Legal pathways for equitable technology transfer and capacity building for AI safety in emerging economies
  *   Legal implications of compliance with ISO/IEC 42001 and ISO/IEC DIS 27090 standards for AI system cybersecurity governance
  *   Obligations regarding the certification and validation of AI systems under ETSI Technical Committee on Securing Artificial Intelligence (TC SAI) standards
  *   Comparative legal analysis of implementation efficacy between NIST AI Risk Management Framework and ISO/IEC 42001 standards in cross-jurisdictional AI deployments

Cross-cutting Topics

  *   Interplay between AI safety, AI security, and AI ethics.
  *   Policy design for global AI safety and security governance.
  *   Standards and certification for trustworthy AI.
  *   Capacity building and technology transfer for AI safety and security in the Global South.

  1.  Submission Guidelines

Research papers — including analytical, theoretical, position papers, or case studies — will be considered for inclusion in the 2026 DAIG Report, even if previously published.

  *   Length: 2,500–5,000 words
  *   Authorship: Up to 3 co-authors
  *   Language: English or Spanish
  *   Style: Author–date, with a reference list at the end of the paper. We strongly recommend using Zotero (www.zotero.org<http://www.zotero.org/>) or another reference manager to insert and manage references, as the required style for future publication may change.
  *   Format: Microsoft Word or OpenDocument Text

Submissions must include:
1. Title
2. Short abstract (200 words)
3. Keywords
4. Draft paper
5. In the body of the email: author’s name, affiliation, and short biographical note

Submissions should be sent to luca.belli [at] fgv.br and walter.gaspar [at] fgv.br, with “Paper Submission DAIG 2025” as the subject line.

Authors will be notified within approximately seven days from the deadline regarding the status of their contributions.

All submissions will be subject to peer review. Each author submitting a paper will be asked to review another submission within 10 days, assessing (1) novelty, (2) theoretical soundness, and (3) quality of presentation. Authors will have the opportunity to revise their contributions based on peer comments.

Selected papers will be published in the 2026 Report of the IGF Coalition on Data and Artificial Intelligence Governance, under open-access Creative Commons licenses.

Authors will also be invited (at their own expense) to present their work at the annual DAIG meeting at the United Nations Internet Governance Forum (IGF), to be held in 2026 (venue and dates to be announced).

[http://www.fgv.br/mailing/2018/direito_rio/assinatura/fgv_direito_rio.jpg]

Luca Belli, PhD

Professor | FGV Law School Rio

Director | Center for Technology & Society

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Botafogo - Rio de Janeiro, RJ - CEP: 22250-900

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