Artificial intelligence (AI) and Machine Learning (ML) have moved from research and pilot projects into everyday business operations, with generative AI accelerating adoption across processes, products, and services. This paper introduces the concept of Responsible AI for organizational practice, with a particular focus on small and medium-sized enterprises. It structures Responsible AI along four focal areas that are central for introducing and operating AI systems in a legally compliant, comprehensible, sustainable, and data-sovereign manner. First, it discusses the EU AI Act as a risk-based regulatory framework, including the distinction between provider and deployer roles and the resulting obligations such as risk assessment, documentation, transparency requirements, and AI literacy measures. Second, it addresses Explainable AI as a basis for transparency and trust, clarifying key notions such as transparency, interpretability, and explainability and summarizing practical approaches to make model behavior and decisions more understandable. Third, it covers Green AI, emphasizing that AI systems should be evaluated not only by performance but also by energy and resource consumption, and outlines levers such as model reuse, resource-efficient adaptation, continuous learning, model compression, and monitoring. Fourth, it examines local models (on-premise and edge) as an operating option that supports data protection, control, low latency, and strategic independence, including domain adaptation via fine-tuning and retrieval-augmented generation. The paper concludes with a consolidated set of next steps for establishing governance, documentation, secure operation, sustainability considerations, and an implementation roadmap.
A Standardized Machine-readable Dataset Documentation Format for Responsible AI
Universitat Oberta de Catalunya（加泰罗尼亚开放大学）；NASA IMPACT（美国国家航空航天局IMPACT）；Google（谷歌）；OpenML；Bayer（拜耳）；Dotphoton；NASA（美国国家航空航天局）；Meta；Oak Ridge National Laboratory（橡树岭国家实验室）；Plaixus Ltd（Plaixus有限公司）；North Carolina State University（北卡罗来纳州立大学）；Graphcore
Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments
Microsoft（微软公司）；Microsoft, AI For Good Research Lab（微软公司AI向善研究实验室）；Microsoft, Research（微软研究院）；Microsoft, Office of Responsible AI（微软公司负责任AI办公室）；Microsoft, Open Innovation（微软公司开放创新部门）
Business and Regulatory Responses to Artificial Intelligence: Dynamic Regulation, Innovation Ecosystems and the Strategic Management of Disruptive Technology
Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding Tube Qualification in Radiology
Microsoft Health Futures（微软健康未来）；UCLH NHS Foundation Trust（伦敦大学学院医院NHS基金会信托）；Canterbury Christ Church University（坎特伯雷基督教会大学）；University College London（伦敦大学学院）；Cambridge University Hospitals NHS Foundation Trust（剑桥大学医院NHS基金会信托）；Microsoft Research Cambridge（微软研究院剑桥）
Who is Responsible? The Data, Models, Users or Regulations? A Comprehensive Survey on Responsible Generative AI for a Sustainable Future
Toronto Metropolitan University（多伦多都会大学）；Vector Institute（矢量研究所）；The University of Central Florida（中佛罗里达大学）；Salim Habib University（萨利姆哈比卜大学）；Information Technology University（信息技术大学）；The University of Texas MD Anderson Cancer Center（德克萨斯大学安德森癌症中心）；Fast School of Computing, National University of Computer and Emerging Sciences（国家计算机与新兴科学大学快速计算机学院）；Queen’s University（女王大学）；Bartin University（巴廷大学）；Saarland University（萨尔兰大学）；Cornell University（康奈尔大学）
