The popularity of artificial intelligence (AI), including machine learning (ML) techniques, has increased in recent years. AI is used in many domains, including cybersecurity, the Internet of Things, and autonomous cars, and is expanding its impact in scientific research, consumer assistants, and enterprise services through advancements in Generative AI (GenAI). Many works have investigated the mathematics and algorithms on which the AI techniques and models are built, but few have examined system engineering as well as their governance, which ensures AI systems are built, used, and managed to maximize benefits and prevent harms. AI engineering and governance needs to bring together diverse stakeholders across AI algorithms, data science, software/system engineering, compliance, legal, and business teams.

In AI software engineering and governance, there is often a gap between high-level abstract principles and low-level concrete tools and rules. Patterns encapsulating recurrent problems and corresponding solutions under particular contexts and pattern languages as organized and coherent patterns can fill such gaps, resulting in a common ``language'' for various stakeholders involved in often interdisciplinary AI software systems development and governance.
Researchers and practitioners study best practices for engineering and governing AI/ML systems to address issues in AI and ML techniques as well as processes, policies, and tools for trustworthy, responsible and safe AI system development and management. Such practices are often formalized as patterns and pattern languages. Major examples are:

• AI architecture and design patterns, such as software engineering patterns for ML applications [Washizaki22], ML design patterns [Lackshmanan20] and agent design patterns [Liu24]
• AI assurance argument patterns, such as safety case patterns for ML systems [Wozniak20] and security argument patterns for DNN [Zeroual23][Mutsche24]
• Responsible AI engineering and governance patterns, such as patterns for creating trustworthy and safe AI systems [Lu23]
• AI development and management practices, such as lifecycle phase practices [Rahman23]
• Prompt engineering patterns such as prompt pattern catalogue and taxonomy [White23][Sasaki24]

While AI engineering and governance patterns have been documented, there's still much to uncover in this landscape. This limited understanding hampers adoption, preventing the realization of their full potential.
The goal of the meeting is to bring together software engineering and AI experts from academia and industry, featuring and taking a special focus on the theoretical, social, technological, and practical advances and issues related to patterns and practices in AI engineering and governance.
The meeting will have two types of sessions:

• The first type of session will have short presentations where each participant will introduce their research work, and list some ideas/challenges/research directions that they would like to discuss during the meeting. Such presentations will be scheduled during the first two days of the meeting.
• The second type of session will consist of intensive discussions among sub-groups of participants. The topics of the discussions will be decided at the meeting among those proposed by participants during their short talks; still, organisers will prepare beforehand some possible topics of discussion. The discussions will proceed on two phases, by first exploring different topics, and then deepening on a restricted set of selected topics.
  • [Exploration phase] In the first instances of this type of session, the meeting will use a dedicated method for guiding the discussion. The current plan is to use a variation of the world café method (http://www.theworldcafe.com/method.html): this will allow participants to move across the different sub-groups and so get to know the different initial topics. In addition to general discussions on the grand challenges and future directions, potential topics also include pattern-specific ones, such as associating existing patterns to organize a pattern system beyond individual areas, mapping patterns and practices onto AI engineering and governance activities, and mining new patterns and practices.
  • Afterwards, each participant will decide which topic they would like to explore. At this stage, some topics that did not get too much interest will be dropped.
  • [Deepening phase] The remaining sessions will be dedicated to intensive discussion on the selected topics. Ultimately, each group should come up with a research agenda for its topic.
  • Afterwards, we are planning to sort out a research agenda for patterns and practices in AI engineering and governance, and plan to make a book proposal through the agenda that is discussed and agreed upon by the participants. We will apply to the “Call for Book Proposals” provided by the Shonan meeting organisation.

[Washizaki22] H. Washizaki, et al. “Software Engineering Design Patterns for Machine Learning Applications,” IEEE Computer 55(3) 2022
[Lackshmanan20] V. Lakshmanan, et al., “Machine Learning Design Patterns,” O’Reilly, 2020
[Wozniak20] E. Wozniak, et al., “A Safety Case Pattern for Systems with Machine Learning Components,” SAFECOMP 2020 Workshop
[Liu22] Liu, Y. et al. ‘Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model based Agents’. AIWare 2024. http://arxiv.org/abs/2405.10467
[Zeroual23] M. Zeroual, et al., “Security Argument patterns for Deep Neural Network Development,” PLoP 2023
[Mutsche24] M. Mutsche, et al. “Robustness-based Security Case Verification for Deep Neural Networks,” AsianPLoP 2024
[Lu23] Q. Lu, L. Zhu, J. Whittle, and X. Xu, “Responsible AI: Best Practices for Creating Trustworthy AI Systems,” Pearson Education, 2023
[Rahman23] M. S. Rahman, et al., “Machine Learning Application Development: Practitioners’ Insights,” Software Quality Journal, 31, 2023
[White23] J, White, et al., “A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT,” arXiv 2302.11382, 2023
[Sasaki24] Y. Sasaki, et al., “A Taxonomy and Review of Prompt Engineering Patterns in Software Engineering,” COMPSAC 2024