Explaining It Your Way - Findings from a Co-Creative Design Workshop on Designing XAI Applications with AI End-Users from the Public Sector
Katharina Weitz, Ruben Schlagowski, Elisabeth André, Maris Männiste, Ceenu George
摘要
Human-Centered AI prioritizes end-users’ needs like transparency and usability. This is vital for applications that affect people’s everyday lives, such as social assessment tasks in the public sector. This paper discusses our pioneering effort to involve public sector AI users in XAI application design through a co-creative workshop with unemployment consultants from Estonia. The workshop’s objectives were identifying user needs and creating novel XAI interfaces for the used AI system. As a result of our user-centered design approach, consultants were able to develop AI interface prototypes that would support them in creating success stories for their clients by getting detailed feedback and suggestions. We present a discussion on the value of co-creative design methods with end-users working in the public sector to improve AI application design and provide a summary of recommendations for practitioners and researchers working on AI systems in the public sector.
问问这篇 Paper
问问你的智能体。
Lune 读过与它相关的顶会 Paper,每个回答都会注明依据哪几篇。
引用它的顶会 Paper6
- Exploring the Needs of Practising Musicians in Co-Creative AI Through Co-DesignStephen James Krol, Maria Teresa Llano Rodriguez, Miguel Loor ParedesCHI 2025 · 被引用 13 次
- "It's Just a Wild, Wild West": Harnessing Public Procurement as an AI Governance MechanismAnna Ida Hudig, Emma Kallina, Jatinder SinghCHI 2026 · 被引用 2 次
- The Promises and Perils of using LLMs for Effective Public ServicesErina Seh-Young Moon, Matthew Tamura, Angelina Zhai, Nuzaira Habib 等CHI 2026 · 被引用 2 次
- Designing a Generative AI-Assisted Music Psychotherapy Tool for Deaf and Hard-of-Hearing IndividualsYoujin Choi, JaeYoung Moon, Jinyoung Yoo, Jennifer G. Kim 等CHI 2026 · 被引用 2 次
- Emergent, not Immanent: A Baradian Reading of Explainable AIFabio Morreale, Joan Serrà, Yuki MitsufujiCHI 2026 · 被引用 1 次
相关 Paper
- "Help Me Help the AI": Understanding How Explainability Can Support Human-AI InteractionSunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong 等CHI 2023 · 被引用 178 次
- Expanding Explainability: Towards Social Transparency in AI systemsUpol Ehsan, Q. Vera Liao, Michael J. Muller, Mark O. Riedl 等CHI 2021 · 被引用 505 次
- Questioning the AI: Informing Design Practices for Explainable AI User ExperiencesQ. Vera Liao, Daniel M. Gruen, Sarah MillerCHI 2020 · 被引用 758 次
- Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI GuidebookNur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg 等CHI 2023 · 被引用 103 次
- Seamful XAI: Operationalizing Seamful Design in Explainable AIUpol Ehsan, Q. Vera Liao, Samir Passi, Mark O. Riedl 等CSCW 2024 · 被引用 41 次
