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。