The AI software lifecycle indicates some shifts in traditional software engineering practices. AI software development involves iterative processes such as model training/testing, validation, and deployment, which differ significantly from conventional software development. Traditional approaches generally emphasize well-formed requirements and deterministic algorithms, whereas AI relies on probabilistic models and continuous learning from historical and user-interaction data. Furthermore, the popularization of AI in the last few years, along with the usage of AI tools and LLMs like Github Copilot, ChatGPT, and Gemini by software teams, also highlights some changes in software development and evolution processes. Integrating AI techniques and tools into AI software processes effectively demands engineers to adopt agile methodologies, focus on data quality and governance, and even incorporate ethical considerations to ensure fairness, reliability, privacy, transparency, sustainability, accountability, and explainability. The evolution from software engineering to AI engineering is crucial for harnessing AI's capabilities and driving long-term innovation responsibly and sustainably.
The goal of WoRTH_AI -- Workshop on Responsible Technology and Human-Centered AI Engineering -- is to share, discuss, debate, and propose advances both to SE4HAI and HAI4SE, emphasizing the premise that a responsible Human-Artificial Intelligence (HAI) based software should improve and facilitate the humans' activities and not to cut-out their workforce. For one-day programming, we invite researchers and practitioners to consider submitting their technical papers on SE4HAI, HAI4SE, or both:
Software processes to develop and evolve responsible HAI systems.
Responsibility, ethics, fairness, transparency, accountability, sustainability, reliability, and explainability in developing and evolving responsible HAI systems.
Governance of HAI software ecosystems.
Impact of the responsible use of LLMs in software process activities, such as requirement elicitation, modeling, designing, coding, testing, and deployment of HAI systems.
Quality assurance of HAI software.
HAI software requirement engineering.
HAI software UI/UX design.
HAI software modeling and designing.
HAI software architecture.
HAI software testing.
CI/CD, DevOps, MLOps, and AIOps of HAI.
Energy sustainability of HAI system lifecycle,
and other related topics.