Decision making in crucial applications such as lending, hiring, and college
admissions has witnessed increasing use of algorithmic models and techniques as
a result of a confluence of factors such as ubiquitous connectivity, ability to
collect, aggregate, and process large amounts of fine-grained data using cloud
computing, and ease of access to applying sophisticated machine learning
models. Quite often, such applications are powered by search and recommendation
systems, which in turn make use of personalized ranking algorithms. At the same
time, there is increasing awareness about the ethical and legal challenges
posed by the use of such data-driven systems. Researchers and practitioners
from different disciplines have recently highlighted the potential for such
systems to discriminate against certain population groups, due to biases in the
datasets utilized for learning their underlying recommendation models. We
present a study of fairness in online personalization settings involving the
ranking of individuals. Starting from a fair warm-start machine-learned model,
we first demonstrate that online personalization can cause the model to learn
to act in an unfair manner if the user is biased in his/her responses. For this
purpose, we construct a stylized model for generating training data with
potentially biased features as well as potentially biased labels and quantify
the extent of bias that is learned by the model when the user responds in a
biased manner as in many real-world scenarios. We then formulate the problem of
learning personalized models under fairness constraints and present a
regularization based approach for mitigating biases in machine learning. We
demonstrate the efficacy of our approach through extensive simulations with
different parameter settings. Code:
this https URL

本文针对在线个性化设置中排名的公平性问题，从公平的机器学习模型开始，探讨了在线个性化如何导致模型不公平，并提出了一种基于正则化的方法来缓解机器学习中的偏差。

公平感知在线个性化

Fairness-Aware Online Personalization

In designing personalized ranking algorithms, it is desirable to encourage a
high precision at the top of the ranked list. Existing methods either seek a
smooth convex surrogate for a non-smooth ranking metric or directly modify
updating procedures to encourage top accuracy. In this work we point out that
these methods do not scale well to a large-scale setting, and this is partly
due to the inaccurate pointwise or pairwise rank estimation. We propose a new
framework for personalized ranking. It uses batch-based rank estimators and
smooth rank-sensitive loss functions. This new batch learning framework leads
to more stable and accurate rank approximations compared to previous work.
Moreover, it enables explicit use of parallel computation to speed up training.
We conduct empirical evaluation on three item recommendation tasks. Our method
shows consistent accuracy improvements over state-of-the-art methods.
Additionally, we observe time efficiency advantages when data scale increases.

提出了一种新的基于批处理的排名估算器和平滑的排名敏感损失函数的个性化排名框架，该框架比先前的工作具有更稳定和准确的排名近似，并且可以通过显式使用并行计算来加速训练，在三个物品推荐任务上取得一致的准确性改进和时间效率优势。