用模糊数学量化AI伦理风险，让系统自动判断哪些行为更符合人类价值观。
ff4ERA: A new Fuzzy Framework for Ethical Risk Assessment in AI
- 融合模糊逻辑与专家判断，将抽象伦理问题转化为可计算的风险分。
- 风险评分随关键因素变化而合理波动，对无关输入保持稳定。
- 适合需要透明、可解释伦理决策的AI系统设计者使用。
协同人工智能（SAI）深化人机协作的同时也带来了更大的伦理风险，如侵犯人权和破坏信任。因此，伦理风险评估（ERA）变得至关重要。然而，现有方法受限于不确定性、模糊性及信息不全，且道德判断具有情境依赖性。为此，本文提出ff4ERA框架，结合模糊逻辑、模糊层次分析法（FAHP）和置信度因子（CF），通过伦理风险评分（ERS）对各类风险进行量化。最终的ERS综合了FAHP权重、传播后的置信度因子与风险等级。案例研究验证了该框架能生成情境敏感、符合伦理的评分，其变化规律与相关因素一致，且对无关输入具备鲁棒性。局部敏感性分析显示响应趋势稳定，全局Sobol分析确认专家设定权重与置信度因子起主导作用，证实模型设计有效。整体结果表明，ff4ERA可生成可解释、可追溯、风险感知的伦理评估，支持假设分析，指导设计者校准隶属函数与专家判断，实现可靠的伦理决策支持。
原文摘要 · Abstract (English)
The emergence of Symbiotic AI (SAI) introduces new challenges to ethical decision-making as it deepens human-AI collaboration. As symbiosis grows, AI systems pose greater ethical risks, including harm to human rights and trust. Ethical Risk Assessment (ERA) thus becomes crucial for guiding decisions that minimize such risks. However, ERA is hindered by uncertainty, vagueness, and incomplete information, and morality itself is context-dependent and imprecise. This motivates the need for a flexible, transparent, yet robust framework for ERA. Our work supports ethical decision-making by quantitatively assessing and prioritizing multiple ethical risks so that artificial agents can select actions aligned with human values and acceptable risk levels. We introduce ff4ERA, a fuzzy framework that integrates Fuzzy Logic, the Fuzzy Analytic Hierarchy Process (FAHP), and Certainty Factors (CF) to quantify ethical risks via an Ethical Risk Score (ERS) for each risk type. The final ERS combines the FAHP-derived weight, propagated CF, and risk level. The framework offers a robust mathematical approach for collaborative ERA modeling and systematic, step-by-step analysis. A case study confirms that ff4ERA yields context-sensitive, ethically meaningful risk scores reflecting both expert input and sensor-based evidence. Risk scores vary consistently with relevant factors while remaining robust to unrelated inputs. Local sensitivity analysis shows predictable, mostly monotonic behavior across perturbations, and global Sobol analysis highlights the dominant influence of expert-defined weights and certainty factors, validating the model design. Overall, the results demonstrate ff4ERA ability to produce interpretable, traceable, and risk-aware ethical assessments, enabling what-if analyses and guiding designers in calibrating membership functions and expert judgments for reliable ethical decision support.
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