论文标题
机器学习在保险中的公平性：一个老人的新破布？
The Fairness of Machine Learning in Insurance: New Rags for an Old Man?
论文作者
论文摘要
自从历史开始以来，众所周知，保险公司使用数据来分类和价格风险。因此，他们很早就面临着与数据相关的公平和歧视问题。随着获取更多颗粒状和行为数据的访问，这个问题变得越来越重要，并且正在不断发展以反映当前的技术和社会问题。通过研究有关歧视的较早辩论，我们表明某些算法偏见是较旧版本的新版本，而另一些算法偏见则显示了先前顺序的逆转。矛盾的是，尽管保险业务并没有深刻变化，也没有大多数偏见，但机器学习时代仍然深深地震撼了保险公平的概念。
Since the beginning of their history, insurers have been known to use data to classify and price risks. As such, they were confronted early on with the problem of fairness and discrimination associated with data. This issue is becoming increasingly important with access to more granular and behavioural data, and is evolving to reflect current technologies and societal concerns. By looking into earlier debates on discrimination, we show that some algorithmic biases are a renewed version of older ones, while others show a reversal of the previous order. Paradoxically, while the insurance practice has not deeply changed nor are most of these biases new, the machine learning era still deeply shakes the conception of insurance fairness.
