The recent release of ChatGPT, Bard, and other large language model (LLM)-based chatbots has drawn huge global attention. The black box nature and the rapid advancements in AI have sparked significant concerns about responsible AI. It is crucial to ensure that the AI systems are developed and used responsibly throughout their entire lifecycle and trusted by humans who are expected to use them and rely on them. To achieve this, a number of AI ethics principles have been published, which AI systems and their applications should conform to. However, high-level AI ethics principles are far from sufficient for ensuring responsible AI systems and applications. There is a significant gap between high-level AI ethics principles and low-level concrete practice for practitioners. Responsible AI challenges can occur at any stage of the AI system development lifecycle, crosscutting AI components, non-AI components, and data components of systems. To tackle the responsible AI challenges, in this special issue, we are looking for cutting edge technologies, novel studies, and promising developments, which can help to inform the community of stakeholders of available actionable approaches to the responsible engineering of AI systems.

Topics of interests include, but are not limited to:

- Requirement engineering for responsible AI
- Software architecture and design of responsible AI systems
- Cybersecurity and privacy technologies for responsible AI
- Verification and validation for responsible AI systems
- DevOps, MLOps, MLSecOps, LLMOps for responsible AI systems
- Human aspect of responsible AI engineering
- Responsible AI assessment tools/techniques and governance
- Trust and trustworthiness in AI systems
- Responsible AI engineering for next-generation foundation model based AI systems (e.g., LLM-based)

Preparation of manuscripts should refer to the guidelines in the “Information for Author” on the IEEE Transactions on Artificial Intelligence website: https://cis.ieee.org/publications/ieee-transactions-on-artificial-intelligence/information-for-authors-tai

Guest Editors
Qinghua Lu, CSIRO, Australia, qinghua.lu@data61.csiro.au
Apostol Vassilev, NIST, USA, apostol.vassilev@nist.gov
Jun Zhu, Tsinghua University, China, dcszj@mail.tsinghua.edu.cn
Foutse Khomh, Polytechnique Montréal, Canada, foutse.khomh@polymtl.ca