AI advancements, especially large language models (LLMs), have sparked concerns about responsible AI and safety. While LLMs are powerful, it's the integration of these models into complex AI systems that has a real-world impact. Ensuring responsible AI requires more than fixing bugs—it demands new engineering approaches. Key challenges include accountability, diversity, and compliance with evolving laws. Colocated with ICSE 2025, this workshop unites experts from various fields to tackle these issues and advance responsible AI engineering, covering requirements, architecture, validation, and operational processes like DevOps and AgentOps.

Topics of interests include, but are not limited to:

Requirement engineering for responsible AI and AI safety
Responsible-AI-by-design and AI-safety-by-design software architecture
Verification and validation for responsible AI and AI safety
DevOps, MLOps, LLMOps, AgentOps for ensuring responsible AI and AI safety
Development processes for responsible and safe AI systems
Responsible AI and AI safety evaluation tools and techniques
Reproducibility and traceability of AI systems
Trust and trustworthiness of AI systems
Responsible AI and AI safety governance
Diversity and Inclusion in the Responsible AI ecosystem; humans, data, processes/algorithms, systems, governance
Operationalization of laws (e.g., EU AI Act) and standards
Human aspect of responsible AI and AI safety engineering
Responsible AI and AI safety engineering for next-generation foundation model-based AI systems (e.g., LLM-based agents)
Case studies from certain high-priority domains (e.g., financial services, scientific discovery, health, environment, energy)
The workshop will be highly interactive, including invited keynotes/talks, paper presentations for different topics in the area of responsible AI engineering.

Two types of contributions will be considered:

- A research or experience full paper with 8 pages max, including references. Papers describing the challenges, starting results, vision papers, or the experience papers from or in cooperation with the practitioners are encouraged.
- A short research or experience paper with 4 pages max, including references. The same topics as for long papers.