合乎伦理且可扩展的自动化：面向商业应用的治理与合规框架
Ethical and Scalable Automation: A Governance and Compliance Framework for Business Applications
AI总结：
针对企业AI应用的伦理、治理与合规风险，本文提出兼顾伦理、可控、可行性与可取性的统一框架，并通过金融、医疗及LLM合成观点案例验证其透明度、性能与可扩展性。
摘要：
AI在企业中的普及应用带来了与伦理原则、治理和法律合规相关的重大挑战。尽管企业已将AI嵌入其日常流程，但它们缺乏一种统一方法来降低其潜在风险。本文提出一个框架，确保AI必须合乎伦理、可控、可行且可取。平衡这些因素可确保设计出一个能够处理其权衡问题的框架，例如在性能与可解释性之间取得平衡。一个成功的框架可为企业提供实用建议，帮助其满足金融和医疗等行业的监管要求；在这些行业中，遵守GPDR和《欧盟AI法案》等标准至关重要。不同案例研究通过在学术和实践环境中整合AI对该框架进行了验证。例如，大型语言模型是生成合成观点的高成本效益替代方案，这些观点可模拟人们对环境问题的态度。这些案例研究表明，拥有结构化框架如何能够提升透明度并维持性能水平，合成分布与预期分布之间的一致性即体现了这一点。这种一致性通过Chi-test分数、归一化互信息和Jaccard指数等指标进行量化。未来研究应进一步探索该框架在多样化工业环境中的实证验证，以确保模型的可扩展性和适应性。
英文摘要：
The popularisation of applying AI in businesses poses significant challenges relating to ethical principles, governance, and legal compliance. Although businesses have embedded AI into their day-to-day processes, they lack a unified approach for mitigating its potential risks. This paper introduces a framework ensuring that AI must be ethical, controllable, viable, and desirable. Balancing these factors ensures the design of a framework that addresses its trade-offs, such as balancing performance against explainability. A successful framework provides practical advice for businesses to meet regulatory requirements in sectors such as finance and healthcare, where it is critical to comply with standards like GPDR and the EU AI Act. Different case studies validate this framework by integrating AI in both academic and practical environments. For instance, large language models are cost-effective alternatives for generating synthetic opinions that emulate attitudes to environmental issues. These case studies demonstrate how having a structured framework could enhance transparency and maintain performance levels as shown from the alignment between synthetic and expected distributions. This alignment is quantified using metrics like Chi-test scores, normalized mutual information, and Jaccard indexes. Future research should explore the framework's empirical validation in diverse industrial settings further, ensuring the model's scalability and adaptability.
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。
