人类服务组织与AI的负责任整合：考量伦理与情境化风险
Human services organizations and the responsible integration of AI: Considering ethics and contextualizing risk(s)
AI总结：
本文提出一个多维风险评估框架，用于在人类服务组织中负责任地整合AI，强调通过情境化考量与本地大语言模型等方案，从低风险应用起步实证探索，以平衡伦理标准与服务能力提升。
摘要：
本文考察了人工智能（AI）在人类服务组织（HSOs）中的负责任整合，提出了一个在多个风险维度上评估AI应用的细致框架。作者认为，关于AI部署的伦理关切——包括专业判断的替代、环境影响、模型偏见以及数据劳工剥削——会因实施情境和具体用例的不同而显著变化。他们挑战了关于AI采纳的二元观点，展示了不同应用如何呈现不同水平的风险，而这些风险往往可以通过谨慎的实施策略得到有效管理。本文强调了有前景的解决方案，如本地大型语言模型，这些方案能够在解决常见伦理关切的同时促进负责任的AI整合。作者提出了一种维度化风险评估方法，该方法考虑了数据敏感性、专业监督要求以及对客户福祉的潜在影响等因素。他们最后概述了一条前进路径，强调实证评估，从低风险应用开始，通过谨慎的实验建立基于证据的理解。这种方法使组织能够在保持高伦理标准的同时，深思熟虑地探索AI如何增强其为客户和社区有效服务的能力。
英文摘要：
This paper examines the responsible integration of artificial intelligence (AI) in human services organizations (HSOs), proposing a nuanced framework for evaluating AI applications across multiple dimensions of risk. The authors argue that ethical concerns about AI deployment -- including professional judgment displacement, environmental impact, model bias, and data laborer exploitation -- vary significantly based on implementation context and specific use cases. They challenge the binary view of AI adoption, demonstrating how different applications present varying levels of risk that can often be effectively managed through careful implementation strategies. The paper highlights promising solutions, such as local large language models, that can facilitate responsible AI integration while addressing common ethical concerns. The authors propose a dimensional risk assessment approach that considers factors like data sensitivity, professional oversight requirements, and potential impact on client wellbeing. They conclude by outlining a path forward that emphasizes empirical evaluation, starting with lower-risk applications and building evidence-based understanding through careful experimentation. This approach enables organizations to maintain high ethical standards while thoughtfully exploring how AI might enhance their capacity to serve clients and communities effectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
