单调性形态制约下的义务伦理学
Deontological Ethics By Monotonicity Shape Constraints
摘要：
我们展示了现代机器学习系统违反常见的义务论伦理原则和社会规范是多么容易，例如“有利于不幸的人”和“不惩罚好的属性”。“我们建议，在某些情况下，可以通过添加形状约束来将这些道德原则纳入机器学习模型中，这些形状约束限制模型仅对相关输入做出积极响应。我们分析了这些义务约束，作用于个人和结果主义群体为基础的公平目标的片面统计平价和机会均等之间的关系。这种策略适用于布尔值或实值的敏感属性，如收入和年龄，并有助于产生更负责任和更值得信赖的人工智能。
英文摘要：
We demonstrate how easy it is for modern machine-learned systems to violate common deontological ethical principles and social norms such as "favor the less fortunate," and "do not penalize good attributes." We propose that in some cases such ethical principles can be incorporated into a machine-learned model by adding shape constraints that constrain the model to respond only positively to relevant inputs. We analyze the relationship between these deontological constraints that act on individuals and the consequentialist group-based fairness goals of one-sided statistical parity and equal opportunity. This strategy works with sensitive attributes that are Boolean or real-valued such as income and age, and can help produce more responsible and trustworthy AI.
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。
