AI-Powered Service Blueprints for Enhancing Human-Centred AI Design Processes Mehrdad Atariani Computer Science University of Law London, United Kingdom mehrdad.atariani89@law.ac.uk Mohamad Saeid Hoseini Industrial Design University of Art Tehran, Iran ms.hoseini@art.ac.ir Abstract is study targets major problems designers face when designing human-AI systems, such as AI literacy gaps and collaborative design challenges. e current research syntheses present the AI-powered service blueprint, a structured tool originating from service design, enables participatory design in which AI systems and stakeholders work together to map the design and integration of AI systems to ensure transparent and user-centred outcomes while being compli- ant with ethical, technical, and legal standards. Despite the need for empirical evaluation, it provides a potential practice for collab- orative design, understanding AI capabilities, and creating more understandable AI applications. is paper is a foundation for fol- lowing empirical research to evaluate using AI-powered blueprints to enhance human-AI interaction in design. CCS Concepts • Human-centered computing;• Human computer interac- tion (HCI);• HCI design and evaluation methods; Keywords AI-powered blueprints, Service Design Tools, HCAI, Human- Centered AI Design ACM Reference Format: Mehrdad Atariani and Mohamad Saeid Hoseini. 2024. AI-Powered Service Blueprints for Enhancing Human-Centred AI Design Processes. In Human Centred Artificial Intelligence - Education and Practice (HCAIep ’24), December 02, 03, 2024, Naples, Italy. ACM, New York, NY, USA, 1 page. https://doi.org/ 10.1145/3701268.3701280 1 BACKGROUND: CHALLENGES IN AI DESIGN In addition to understanding and designing around AI capabili- ties, designers working with AI systems oſten face many other challenges including difficulty in understanding how AI makes decisions, limited technical knowledge of AI [1], and intricate inter- action between humans and AI [2]. It is critical to understand and design around AI capabilities as human-centred AI design follows AI techniques with the goal of producing technically proficient systems that are also desirable, trustworthy, and ethical. e lack Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). HCAIep ’24, December 02, 03, 2024, Naples, Italy © 2024 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-1159-6/24/12 https://doi.org/10.1145/3701268.3701280 of clear communication of AI capabilities imposes challenges on designers and stakeholders to visualise more forceful solutions that are in touch with human preferences. 2 METHODS is is a qualitative systematic literature review of 217 studies pub- lished between 2010 and 2024 from sources like the IEEE Xplore and ACM Digital Library, and we explored the challenges of AI capabilities integration into human-centred design processes. e proposed concept of this study addresses the lack of tools for bet- ter understanding and integration of AI capabilities and overcome design early-stage challenges by enhancing interdisciplinary col- laboration, AI literacy, and explainability and transparency of AI Systems. 3 KEY FINDINGS AND CONCLUSION AI-driven blueprints enable designers to gain a beer understanding of AI capabilities [3], facilitate participatory design within teams [4], and ensure AI systems adhere to ethical, legal, and technical standards, making them more trustworthy and user-friendly [5]. Despite its potential, future studies should also carry forward the creation and application of blueprints in different design domains to determine their usability and utility. Furthermore, it might be also useful to expand this paradigm with AI auditing tools to check compliance with ethical principles, especially that nowadays AI is used more and more oſten. References [1] Freya Smith, Malak Sadek, and Céline Mougenot. 2023. Empowering end-users in co-designing AI: An AI literacy card-based toolkit for non-technical audiences. In Electronic Workshops in Computing, 2023. BCS Learning & Development. [2] Qian Yang, Aaron Steinfeld, Carolyn Rosé, and John Zimmerman. 2020. Re- examining whether, why, and how human-AI interaction is uniquely difficult to design. In Proceedings of the 2020 CHI Conference on Human Factors in Comput- ing Systems, 2020. ACM, New York, NY, USA. [3] Jennifer Villareale and Jichen Zhu. 2021. Understanding mental models of AI through player-AI interaction. arXiv [cs.HC]. Retrieved from http://arxiv.org/abs/ 2103.16168 [4] L. Chong, K. Kotovsky, and J. Cagan. 2022. Are confident designers good teammates to artificial intelligence?: A study of self-confidence, competence, and collaborative performance. Proc. Des. Soc. 2, (2022), 1531–1540. https://doi.org/10.1017/pds.2022. 155 [5] Upol Ehsan and Mark O. Riedl. 2020. Human-centered Explainable AI: Towards a reflective sociotechnical approach. arXiv [cs.HC]. Retrieved from http://arxiv.org/ abs/2002.01092