生成阅读讨论问题

Use the following three questions to guide discussion. For each, ask students to cite specific details from the text (e.g., chains, fire, shadows, ascent, sun) and explain their reasoning.

1. Decode the symbols

• Prompt: What does each element of the cave allegory symbolize in Plato’s account of knowledge and education (chains, shadows, fire, ascent, sun, return)?
• Guide students to map symbols to stages of cognition (imagination, belief, thought, understanding) and to educational processes (turning the soul, dialectic).
• Ask: Where do mathematics and dialectic fit in the ascent? What does the sun represent beyond mere “truth”?

2. Examine the educator’s duty and resistance to learning

• Prompt: Why does the freed prisoner have to return to the cave, and what does the reaction of the prisoners suggest about the challenges educators face?
• Push for text-based evidence about ridicule, hostility, and the difficulty of adjusting eyes to light/dark.
• Ask: What responsibilities do knowledgeable individuals have to those still “in the cave”? How should teachers handle learners’ discomfort or resistance during intellectual “turning”?

3. Apply the allegory to contemporary learning

• Prompt: Identify a modern “shadow-world” (e.g., social media feeds, misinformation, test-prep routines, echo chambers). How does education function as the “turning of the soul” in that context?
• Require a concrete classroom strategy (e.g., source triangulation, Socratic questioning, data visualization, productive struggle) and criteria for judging whether students have actually “turned” rather than just memorized.
• Ask: What signs would show genuine understanding (seeing the “sun”) versus reliance on appearances (shadows)?

以下讨论问题旨在引导学生基于阅读材料进行深入且可操作的分析。请在讨论中引用阅读材料中的概念、论证或案例，并尽量标注页码或段落位置。

1. 公平与偏见：在特定应用场景中，何为“可接受的公平”？

• 明确概念：用阅读材料中的至少两种公平标准进行界定（如个体公平、群体公平），指出它们可能产生冲突的条件。
• 情境聚焦：选择一个高风险场景（如招聘、信贷、医疗），梳理受影响的利益相关者及其风险。
• 提出权衡：识别数据偏差、模型偏差、部署偏差各一例，并各给出1项可操作干预；评估预期副作用（如准确率变化、不同群体误差差距）。
• 设定指标：给出用于判断“达到公平门槛”的可量化指标与停止标准，并说明为什么这些指标具有可解释性与可操作性。

2. 数据隐私与知情同意：何时“合法使用数据”仍可能“不合乎伦理”？

• 区分边界：根据阅读材料区分法律合规与伦理原则（如数据最小化、目的限制、可撤回同意），并指出两者可能不一致的情形。
• 利益权衡：列出至少三类利益相关者（数据主体、模型提供方、下游用户），分析其权利与风险。
• 方案设计：提出至少两项技术或流程性方案（如差分隐私/联邦学习、数据溯源与退出机制、用途审计与数据治理协议），分析其对模型性能、透明度与可审计性的影响。
• 决策准则：制定一份“何时必须取得明确同意、何时可在严格条件下豁免”的判准清单，并说明审计与问责方式。

3. 责任与可追责性：当AI决策造成伤害时，谁对什么负责？

• 绘制链条：基于阅读材料识别从数据收集—建模—评估—部署—监控的责任环节，明确每一环节的职责边界。
• 证据留痕：为每一环节设定至少一项可审计证据（如数据版本与来源记录、模型卡与风险评估、红队测试报告、变更日志、用户告知与同意记录）。
• 比较机制：对比“事前治理”（标准、认证、影响评估）与“事后治理”（责任保险、赔偿、强制召回）的适用条件、优缺点。
• 形成立场：给出你主张的责任分配方案，提出两条反对意见并逐一回应，说明在现实约束下如何落地执行与监督。