Joint Workshop

Security, Human Awareness, and Risk Mitigation in AI-driven Systems

SHIELD-AI 2026 is an interdisciplinary workshop on AI risk management, human–AI interaction, and human-centered cybersecurity in adversarial and security-critical environments.

Held within ECML PKDD 2026
7–11 September 2026 · Naples, Italy

7–11 September 2026

About the workshop

Artificial Intelligence systems are increasingly integrated into everyday applications, digital infrastructures, and security-critical environments. While these systems offer significant benefits, they also introduce systemic risks related to technical vulnerabilities, algorithmic bias, integration issues in complex systems, and the dynamics of human–AI interaction.

The workshop is held within ECML PKDD 2026 — the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases — taking place in Naples, Italy, from 7 to 11 September 2026.

At the same time, modern cyber threats have shifted toward the cognitive domain, exploiting human behavior, social dynamics, and large-scale information ecosystems. Social cyber attacks such as phishing, social engineering, and disinformation campaigns manipulate trust mechanisms, cognitive biases, and online communication patterns.

SHIELD-AI 2026 provides an interdisciplinary forum that brings together researchers and practitioners from machine learning, knowledge discovery, cybersecurity, and human-centered computing. The workshop aims to advance methods for identifying, modeling, and mitigating risks in AI-enabled socio-technical systems operating in adversarial and human-critical environments.

Important dates

5 June 2026

Paper Submission

1 July 2026

Paper Acceptance

10 July 2026

Camera-ready Papers

Half-day event

Workshop format

Topics of interest

Risk identification and modeling in AI-driven systems and multi-component ecosystems.
Human–AI interaction risks, trust, overreliance, and decision-making under automation.
Human-centered cybersecurity and social cyber attacks, including phishing, social engineering, and disinformation.
Machine learning and knowledge discovery for cyber threat detection on large-scale heterogeneous data.
Natural language processing and large language models for malicious or manipulative content analysis.
Graph learning and knowledge graphs for social networks, communication patterns, and cyber threat ecosystems.
Bias, robustness, uncertainty, and adversarial machine learning in human-critical settings.
Explainable and trustworthy AI for security-critical applications.
Generative AI for both cyber attacks and defensive analysis.
Runtime monitoring, continual risk assessment, and adaptive mitigation strategies.
Human behavioral and psychophysiological data analysis.
Privacy, data protection, cyber threat intelligence, and knowledge sharing.
Human-in-the-loop and reasoning-based methods for dynamic risk assessment and explanation.
Case studies and empirical evaluations in real-world deployments.

Submission guidelines

Papers must be written in English and formatted in LaTeX following the Springer LNCS author kit. The maximum length is 16 pages including references. Over-length papers will be rejected without review.

Up to 10 MB of additional material may be uploaded with the submission. Appendices should be submitted separately, and the main paper must remain self-contained within the page limit.

SHIELD-AI 2026 welcomes regular research papers, survey articles, mature work, recent research contributions, preliminary exploratory work, and industry experience or case-study papers. Submissions must be original and not under review elsewhere.

All submissions should be made in PDF via Microsoft CMT and must adhere to the Springer LNCS style. At least one author of each accepted paper must register and present the paper in person in Naples.

The workshop papers may be included in a joint post-workshop proceeding published by Springer Communications in Computer and Information Science (CCIS), with authors able to opt in or out.

Submission link: TBD.