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.

翻译：人工智能在商业领域的普及应用，带来了伦理准则、治理机制和法律合规方面的重大挑战。尽管企业已将人工智能嵌入日常业务流程，但在缓解其潜在风险方面仍缺乏统一的方法。本文提出一个确保人工智能必须符合伦理、可控、可行且合意的框架。平衡这些要素能够保证所设计的框架能够处理其内在权衡，例如在性能与可解释性之间取得平衡。一个成功的框架为企业提供了满足金融、医疗等关键行业监管要求的实践指导，这些领域必须遵守《通用数据保护条例》和《欧盟人工智能法案》等标准。多个案例研究通过在学术与实践环境中集成人工智能，验证了该框架的有效性。例如，大语言模型可作为生成模拟环境问题态度的合成观点的成本效益方案。这些案例研究表明，结构化框架如何能够提升透明度并保持性能水平，这一点通过合成分布与期望分布之间的对齐得以体现。该对齐程度通过卡方检验分数、归一化互信息和杰卡德指数等指标进行量化。未来研究应在多样化工业场景中进一步探索该框架的实证验证，以确保模型的可扩展性与适应性。