解读社会尊重：ML模型的规范视角
Interpreting Social Respect: A Normative Lens for ML Models
摘要：
机器学习通常被视为一个内在的价值中立的过程：训练输入中的统计趋势“简单地”用于推广到新的示例。然而，当模型影响社会系统（如人类之间的互动）时，模型学习到的这些模式具有规范性意义。重要的是，我们不仅要问“数据中存在什么模式？”，而且“我们希望我们的系统如何影响人们？“特别是，由于社会中的少数群体和边缘化成员在数据集中的代表性往往不足，模型可能会对这些群体产生不良的不同影响。因此，社会公平和分配正义的目标要求我们开发工具来识别和解释模型引入的危害。
英文摘要：
Machine learning is often viewed as an inherently value-neutral process: statistical tendencies in the training inputs are "simply" used to generalize to new examples. However when models impact social systems such as interactions between humans, these patterns learned by models have normative implications. It is important that we ask not only "what patterns exist in the data?", but also "how do we want our system to impact people?" In particular, because minority and marginalized members of society are often statistically underrepresented in data sets, models may have undesirable disparate impact on such groups. As such, objectives of social equity and distributive justice require that we develop tools for both identifying and interpreting harms introduced by models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
