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.

本文介绍一种使用多任务神经网络架构进行索赔预测的技术解决方案，该模型能够使用只有部分受保护特征信息的数据进行训练，并且生成的价格不受间接歧视影响，且其性能在有部分缺失政策持有人信息的情况下具有明显的优势。

计算无歧视保险价格的多任务网络方法

A multi-task network approach for calculating discrimination-free insurance prices

Artificial Intelligence (AI) is increasingly used to make important decisions
about people. While issues of AI bias and proxy discrimination are well
explored, less focus has been paid to the harms created by profiling based on
groups that do not map to or correlate with legally protected groups such as
sex or ethnicity. This raises a question: are existing equality laws able to
protect against emergent AI-driven inequality? This article examines the legal
status of algorithmic groups in North American and European non-discrimination
doctrine, law, and jurisprudence and will show that algorithmic groups are not
comparable to traditional protected groups. Nonetheless, these new groups are
worthy of protection. I propose a new theory of harm - "the theory of
artificial immutability" - that aims to bring AI groups within the scope of the
law. My theory describes how algorithmic groups act as de facto immutable
characteristics in practice that limit people's autonomy and prevent them from
achieving important goals.

通过分析北美和欧洲的《非歧视条例》、法律和判例，本文认为算法群体不可比较于传统的受保护群体，但这些新群体同样值得保护。作者提出 “人工不变理论”，以保护算法群体，以防止其在实践中成为事实上的不可改变特征，从而限制人们的自治权并阻止其实现重要目标。

人工不变理论：在反歧视法下保护算法群体

The Theory of Artificial Immutability: Protecting Algorithmic Groups Under Anti-Discrimination Law

Machine learnt systems inherit biases against protected classes, historically
disparaged groups, from training data. Usually, these biases are not explicit,
they rely on subtle correlations discovered by training algorithms, and are
therefore difficult to detect. We formalize proxy discrimination in data-driven
systems, a class of properties indicative of bias, as the presence of protected
class correlates that have causal influence on the system's output. We evaluate
an implementation on a corpus of social datasets, demonstrating how to validate
systems against these properties and to repair violations where they occur.

该研究旨在解决机器学习算法在训练中自然而然会对历史上被歧视的群体产生偏差的问题，并通过实验证明了可以通过验证和修复违规来解决这些问题。