Engineering Safety-Critical AI Systems

AAAI Symposium 2025 • Building AI Systems for Safety-Critical Applications

Duration

2.5 Days

Format

Keynotes, Papers & Panels

Submission Deadline

August 11, 2025

Notification

August 22, 2025

Submit Paper Learn More

About the Symposium

Artificial intelligence has increasing application to high-risk settings, but foundational practices for engineering safe AI systems remain few, and research in safety engineering for AI remains scattered across disparate fields of study.

This symposium strikes at a fundamental question: How should we build AI systems for safety-critical applications?

We bring together three communities: domain experts who want to use AI in safety-critical applications, AI researchers and practitioners who build AI capabilities, and safety and systems engineers who design, develop, and test systems that include AI components.

Important Dates

Submission Deadline

August 11

All papers must be submitted through AAAI EasyChair by 11:59 PM UTC

Notification

August 22

Authors will be notified of acceptance or rejection decisions

Camera-Ready

August 29

Final versions of accepted papers due for proceedings

Topics of Interest

While most topics around safety and AI are welcome, we are especially interested in topics that inform how to engineer AI systems now and in the future:

Safety Requirements Engineering

Safety requirements engineering for AI and/or new safety standards for AI systems

Software Architectures

Software architectures for increased AI system safety

Uncertainty & Robustness

Uncertainty quantification and/or robustness in AI components or systems

System Specifications

Methods for defining AI system specifications

Testing & Evaluation

Safety test and evaluation of AI components or systems

Safety Tooling

Software tooling to support safety engineering in AI

Failure Analysis

Reporting of high impact failure modes or cases in AI systems

Domain Applications

High-risk application domains of AI that require specific definitions of safety

Case Studies

Case studies of safety engineering for deployed AI systems

Formal Verification

Formal specification and verification of AI systems and/or processes for certifying AI system safety

Safe-by-Design AI

Provably safe AI and safe-by-design AI approaches

Human-AI Interaction

Human-AI interaction in safety-critical systems

Submission Tracks

1Research and Development

This track seeks papers articulating new AI safety research or technical reports describing new safety engineering artifacts (process, procedure, standards, software architectures, tooling, etc.).

Preference will be given to works that are:

  • Rigorous and well-evidenced
  • Have evidence to support real-world application
  • Advance the discipline of engineering safe AI systems

2Case Studies in Engineering AI Systems

This track seeks papers that highlight the practical engineering challenges with building safe AI systems in a challenging application domain and/or present a case study in engineering safe AI systems in the real world.

Preference will be given to works that:

  • Showcase real-world problems with high impact
  • Present rigorous and well-justified engineering solutions

Submission Requirements

Paper Formats

  • Short papers: 2-4 pages (excluding references)
  • Full papers: 6-8 pages (excluding references)

Submission Details

  • Use AAAI-25 author kit formatting
  • Submit through AAAI EasyChair site
  • Single-blind review (papers not anonymized)
Submit via EasyChair

Symposium Committee

Co-Chairs

Wanyi Chen

Co-Chair
Duke University

wc151@duke.edu

Dr. Eric Heim

Co-Chair
Carnegie Mellon University

etheim@sei.cmu.edu

Organizing Committee

Dr. Gregory Canal

Johns Hopkins University

Greg.Canal@jhuapl.edu

Dr. Mary Cummings

George Mason University

cummings@gmu.edu

Andrew Dolgert

Carnegie Mellon University

ajdolgert@sei.cmu.edu

Dr. Lu Feng

University of Virginia

lf9u@virginia.edu

Ritwik Gupta

University of Maryland & UC Berkeley

ritwikgupta@berkeley.edu

Chase Midler

CrowdStrike

cmidler@gmail.com

Dr. Sanjeev Mohindra

MIT Lincoln Laboratory

smohindra@ll.mit.edu