人工智能偏见测验

当您参加测验时，就会出现答案和解释。

1. Where can bias enter an AI system according to the guide?
   • Data, measurement, modeling choices, human judgments, and the surrounding workflow
   • Only through a sensitive column
   • Only through the user interface
   • Only after a model is deployed
2. 为什么删除敏感属性无法消除偏见？
   • Sensitive attributes are always the only source
   • Other features or historical labels may encode related patterns
   • Removing columns guarantees fairness
   • Model evaluation no longer needs groups
3. 数据集的标签反映了过去的人类决定。团队应该问什么？
   • Whether the sensitive column was deleted
   • Whether the label is easy to predict
   • Whether the label captures the intended outcome or an old process
   • Whether the model has enough layers
4. 为什么要报告子组指标的样本量和不确定性？
   • An average score is always sufficient
   • Every subgroup has the same sample size
   • Uncertainty makes group testing impossible
   • Small groups can have unreliable estimates and averages can hide disparities
5. 为什么选择一种公平标准就无法解决其他问题？
   • One statistical criterion settles every legal concern
   • Different criteria answer different questions and may conflict
   • Any single metric guarantees fairness in every context
   • Choosing a metric removes the need for affected-community input
6. 谁应该帮助选择后果性系统的公平标准？
   • Only the engineering team
   • 仅型号供应商
   • Domain experts and people affected by the system
   • A language model without review
7. Why review the workflow around a model?
   • How predictions are used and challenged can change the distribution of harm
   • The model output fully determines fairness
   • Appeals cannot affect outcomes
   • Workflow has no effect after training
8. What should a team do after applying a bias mitigation?
   • Hide subgroup results
   • Assume the mitigation certifies fairness
   • Stop monitoring
   • Evaluate its intended effect and possible new problems