算法偏见通常从哪里来？
Where does algorithmic bias usually come from?
偏见多源于有偏的训练数据、被放大的历史歧视、目标函数只优化点击等窄指标，以及设计者的隐含假设；它并不“中立”，需审计与多样数据来纠正。
Bias often comes from skewed training data, amplified historical discrimination, narrow goals like click optimization, and designers' hidden assumptions; it isn't “neutral” and needs auditing and diverse data to correct.
难度 专业 · 适合 高中(15-18)
CC BY 4.0 · 已核验 · Verified 2026-10-04
来源 · Source
Barocas & Selbst,《Big Data's Disparate Impact》(California Law Review, 2016)
Solon Barocas & Andrew Selbst, 'Big Data's Disparate Impact' (California Law Review, 2016)
独立核验依据 · Independent verification: 核验依据 1 DOI 10.2139/ssrn.2477899
#算法偏见 #数据
算法推荐如何塑造人们看到的信息？
How does algorithmic recommendation shape the information people see?
推荐系统按用户过往行为推送相似内容以提升停留，可能形成“信息茧房”与极端化；它既满足兴趣，也限制了偶遇不同观点的机会。
Recommenders push similar content by past behavior to boost engagement, possibly creating “filter bubbles” and polarization; they satisfy interests but limit chance encounters with differing views.
难度 进阶 · 适合 大众
CC BY 4.0 · 已核验 · Verified 2026-11-03
来源 · Source
Eli Pariser《信息茧房:互联网正在隐藏什么》,Penguin,2011
Eli Pariser, The Filter Bubble: What the Internet Is Hiding from You, Penguin Press, 2011
独立核验依据 · Independent verification: 核验依据 1 ARCHIVE 标识 filterbubblewhat0000pari_z3l4：The filter bubb
#信息茧房 #推荐
什么是“算法社会”？
What is an "algorithmic society"?
算法社会指日常决策——如新闻推送、贷款审批、招聘筛选——越来越多由算法自动完成，技术逻辑深度嵌入社会运行。
An algorithmic society is one where daily decisions—news feeds, loan approvals, hiring screens—are increasingly automated, with technical logic woven into social life.
难度 入门 · 适合 大众
CC BY 4.0 · 来源待核验 · Source pending review
来源 · Source
出处备注：本题原挂出处与答案内容不符，已撤回；具体书目待补，暂不展示来源。
本题原挂出处与答案内容不符，已撤回；具体书目待补。
Previous citation withdrawn as unsupported for this answer; a specific source is pending.
#算法社会 #自动化