As the use of machine learning (ML) models in product development and data-driven decision-making processes became pervasive in many domains, people's focus on building a well-performing model has increasingly shifted to understanding how their model works. While scholarly interest in model interpretability has grown rapidly in research communities like HCI, ML, and beyond, little is known about how practitioners perceive and aim to provide interpretability in the context of their existing workflows. This lack of understanding of interpretability as practiced may prevent interpretability research from addressing important needs, or lead to unrealistic solutions. To bridge this gap, we conducted 22 semi-structured interviews with industry practitioners to understand how they conceive of and design for interpretability while they plan, build, and use their models. Based on a qualitative analysis of our results, we differentiate interpretability roles, processes, goals and strategies as they exist within organizations making heavy use of ML models. The characterization of interpretability work that emerges from our analysis suggests that model interpretability frequently involves cooperation and mental model comparison between people in different roles, often aimed at building trust not only between people and models but also between people within the organization. We present implications for design that discuss gaps between the interpretability challenges that practitioners face in their practice and approaches proposed in the literature, highlighting possible research directions that can better address real-world needs.
"It Might be Technically Impressive, But It's Practically Useless to us": Motivations, Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industry
Human-Computer Interaction Institute, Carnegie Mellon University（卡内基梅隆大学人机交互研究所）；Tsinghua University（清华大学）；Reuters Institute for the Study of Journalism, University of Oxford（牛津大学路透新闻研究所）；School of Journalism and Mass Communication, University of Iowa（爱荷华大学新闻与大众传播学院）
Generative AI Assistants in Software Development Education: A vision for integrating Generative AI into educational practice, not instinctively defending against it
Newcastle University（纽卡斯尔大学）
针对生成式 AI 编程助手带来的行业变革，本文通过访谈从业者了解实践与挑战，提出将 GAI 融入软件开发教育的愿景及教学建议。
Bangladesh AI Readiness: Perspectives from the Academia, Industry, and Government
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Dhaka（达卡大学）；S.M.R. Law College, Jessore（谢霍尔SMR法学院）；North South University（南北大学）；Augmedix Bangladesh（奥格美迪克斯孟加拉国）；University of Toronto（多伦多大学）
Applying Ontologies and Knowledge Augmented Large Language Models to Industrial Automation: A Decision-Making Guidance for Achieving Human-Robot Collaboration in Industry 5.0
