非表示:
キーワード:
Computer Science, Learning, cs.LG,Computer Science, Information Retrieval, cs.IR,Statistics, Machine Learning, stat.ML
要旨:
People are rated and ranked, towards algorithmic decision making in an
increasing number of applications, typically based on machine learning.
Research on how to incorporate fairness into such tasks has prevalently pursued
the paradigm of group fairness: ensuring that each ethnic or social group
receives its fair share in the outcome of classifiers and rankings. In
contrast, the alternative paradigm of individual fairness has received
relatively little attention. This paper introduces a method for
probabilistically clustering user records into a low-rank representation that
captures individual fairness yet also achieves high accuracy in classification
and regression models. Our notion of individual fairness requires that users
who are similar in all task-relevant attributes such as job qualification, and
disregarding all potentially discriminating attributes such as gender, should
have similar outcomes. Since the case for fairness is ubiquitous across many
tasks, we aim to learn general representations that can be applied to arbitrary
downstream use-cases. We demonstrate the versatility of our method by applying
it to classification and learning-to-rank tasks on two real-world datasets. Our
experiments show substantial improvements over the best prior work for this
setting.
