以下为“人工智能伦理”模块的三条讨论问题（中英双语），旨在引导高质量、基于证据的专业讨论。每条问题包含聚焦点，以支持结构化分析与论证。
1) 主题：公平性与偏见缓解（应用情境：信贷评分/招聘筛选）
- 问题（中文）：在高影响力决策场景中，如何在准确性、不同公平性指标（如差异影响率、机会均等）与潜在伤害之间进行权衡？请提出一份可执行的偏见审计与缓解方案，明确数据、模型与后处理三个层面的具体措施与监控指标。
- Guidance（English）：In high-stakes decisions, how should trade-offs be made among predictive accuracy, fairness metrics (e.g., disparate impact, equal opportunity), and potential harms? Propose an actionable bias audit and mitigation plan, specifying concrete measures and monitoring metrics across data, model, and post-processing layers.
讨论聚焦点 / Discussion foci:
- 数据层：采样不均与标签偏差的检测与修复；特征的必要性与敏感属性处理策略。
Data: Detect/repair sampling and label bias; feature necessity; handling of sensitive attributes.
- 模型层：约束优化或对抗去偏等方法的适用性与风险。
Model: Suitability and risks of constrained optimization or adversarial debiasing.
- 后处理层：阈值调整、群体级/个体级校准与合规影响。
Post-processing: Thresholding, group/individual calibration, and compliance impact.
- 监控：上线后的漂移监测、告警阈值、审计频率与记录要求。
Monitoring: Drift detection, alert thresholds, audit cadence, and logging requirements.
2) 主题：责任分配与可追溯性（全生命周期治理）
- 问题（中文）：当AI系统造成不良影响（例如错误拒绝贷款或医疗误判）时，如何界定数据提供方、模型开发方、部署方与业务使用方的责任边界？请构建一份生命周期RACI矩阵（或等效机制），并说明人机协作（HITL）、升级路径与事故响应流程如何落地。
- Guidance（English）：When an AI system causes adverse outcomes (e.g., wrongful loan denial or clinical misclassification), how should accountability be apportioned among data providers, model developers, deployers, and business users? Develop a lifecycle RACI (or equivalent) and explain how HITL checkpoints, escalation paths, and incident response will be operationalized.
讨论聚焦点 / Discussion foci:
- 工件与证据：数据说明书、模型卡、评测报告与变更日志的最低合规证明清单。
Artifacts/evidence: Datasheets, model cards, evaluation reports, and change logs as minimum proof.
- 决策控制：人工复核阈值、可逆决策与用户申诉通道设计。
Decision controls: Human-review thresholds, reversibility, and user appeal mechanisms.
- 供应链风险：第三方模型/数据的尽职调查、合同义务与停用/回滚策略。
Supply chain risk: Due diligence for third-party models/data, contractual obligations, and rollback plans.
- 安全与隐私：访问控制、审计追踪与最小化数据保留策略。
Security/privacy: Access control, audit trails, and data minimization/retention.
3) 主题：透明度、可解释性与隐私的平衡（监管与实践）
- 问题（中文）：在需要向不同利益相关方（终端用户、监管者、内部审计）提供“有意义信息”时，如何在模型性能、可解释性与隐私保护之间做出设计取舍？请为一个生成式AI或高风险分类模型提出分层解释与披露策略，并定义可测量的解释质量与隐私泄露风险指标。
- Guidance（English）：When providing “meaningful information” to diverse stakeholders (end users, regulators, internal audit), how should design choices balance model performance, explainability, and privacy protection? For a generative AI or high-risk classifier, propose a layered explanation and disclosure strategy, and define measurable metrics for explanation quality and privacy leakage risk.
讨论聚焦点 / Discussion foci:
- 解释方法：全局与局部解释的选择条件与局限（例如特征重要性、反事实解释）。
Methods: Conditions/limitations for global vs. local explanations (e.g., feature importance, counterfactuals).
- 披露与沟通：用途边界、已知限制、数据来源与合成内容标识的用户可理解表述。
Disclosure: Usage boundaries, known limitations, data provenance, and synthetic content labeling in plain language.
- 隐私保护：差分隐私、检索增强生成的最小暴露策略与成员推断攻击防护。
Privacy: Differential privacy, minimal exposure in RAG, and protection against membership inference.
- 评估指标：解释稳定性、忠实度、可用性（面向非技术受众）与隐私风险评分。
Metrics: Stability, fidelity, usability (for non-technical users), and privacy risk scoring.
