In applications of predictive modeling, such as insurance pricing, indirect or proxy discrimination is an issue of major concern. Namely, there exists the possibility that protected policyholder characteristics are implicitly inferred from non-protected ones by predictive models, and are thus having an undesirable (or illegal) impact on prices. A technical solution to this problem relies on building a best-estimate model using all policyholder characteristics (including protected ones) and then averaging out the protected characteristics for calculating individual prices. However, such approaches require full knowledge of policyholders' protected characteristics, which may in itself be problematic. Here, we address this issue by using a multi-task neural network architecture for claim predictions, which can be trained using only partial information on protected characteristics, and it produces prices that are free from proxy discrimination. We demonstrate the use of the proposed model and we find that its predictive accuracy is comparable to a conventional feedforward neural network (on full information). However, this multi-task network has clearly superior performance in the case of partially missing policyholder information.
翻译:在诸如保险定价、间接或代理歧视等预测性模型的应用中,存在一个令人关切的主要问题。也就是说,有可能通过预测性模型从非保护性模型中隐含地推断出受保护的投保人特点,从而对价格产生不可取(或非法)的影响。这个问题的技术解决办法依赖于利用所有投保人特点(包括受保护的模型)建立一个最准确的估计模型,然后在计算个人价格时平均使用受保护的特征。然而,这种方法需要充分了解投保人受保护的特点,而这本身可能存在问题。在这里,我们通过使用多任务神经网络结构来解决这一问题,只能使用部分关于受保护特征的信息进行培训,并产生不受替代歧视的价格。我们展示了使用拟议模型的情况,我们发现其预测准确性与传统的向前神经网络(关于完整信息)相当。然而,在部分缺失投保人信息的情况下,这一多任务网络显然具有优越性。