📄 中文摘要
在大型语言模型和生成架构的快速发展中，开发者社区对预测能力的过度追求，导致了对因果理解的忽视。作为一名数据科学家和医学博士，作者在NHS的癌症商业智能领域工作，并在TalentHacked担任联合创始人，亲眼目睹了黑箱模型在高风险环境中的失败。无论是预测患者中风风险的算法，还是决定技术专业人士是否符合英国全球人才签证资格的系统，缺乏解释能力的模型不仅是技术债务，更是系统性负担。需要转变思路，从构建“神谕”转向构建“合作伙伴”。
可解释性的重要性：为什么黑箱AI在高风险系统中是战略负担
📄 English Summary
The Interpretability Imperative: Why Black-Box AI is a Strategic Liability in High-Stakes Systems
In the current surge of Large Language Models (LLMs) and generative architectures, the developer community has developed an unhealthy obsession with predictive power, neglecting causal understanding. The author, a Data Scientist and Medical Doctor who transitioned into Cancer Business Intelligence for the NHS and is now a Co-Founder at TalentHacked, has witnessed the failures of black-box models in real-world, high-stakes environments. Whether predicting a patient's stroke risk or determining a tech professional's eligibility for a UK Global Talent Visa, models that cannot explain their reasoning represent not just technical debt but a systemic liability. A shift is needed from building Oracles to building Collaborators.