Machine learning relies on the assumption that unseen test instances of a
classification problem follow the same distribution as observed training data.
However, this principle can break down when machine learning is used to make
important decisions about the welfare (employment, education, health) of
strategic individuals. Knowing information about the classifier, such
individuals may manipulate their attributes in order to obtain a better
classification outcome. As a result of this behavior---often referred to as
gaming---the performance of the classifier may deteriorate sharply. Indeed,
gaming is a well-known obstacle for using machine learning methods in practice;
in financial policy-making, the problem is widely known as Goodhart's law. In
this paper, we formalize the problem, and pursue algorithms for learning
classifiers that are robust to gaming.
We model classification as a sequential game between a player named "Jury"
and a player named "Contestant." Jury designs a classifier, and Contestant
receives an input to the classifier, which he may change at some cost. Jury's
goal is to achieve high classification accuracy with respect to Contestant's
original input and some underlying target classification function. Contestant's
goal is to achieve a favorable classification outcome while taking into account
the cost of achieving it.
For a natural class of cost functions, we obtain computationally efficient
learning algorithms which are near-optimal. Surprisingly, our algorithms are
efficient even on concept classes that are computationally hard to learn. For
general cost functions, designing an approximately optimal strategy-proof
classifier, for inverse-polynomial approximation, is NP-hard.

机器学习中的好斗现象及其解决方案的研究，通过将分类问题建模为一种游戏的方式，提出了一种近乎最优的、能够抵抗 “好斗” 现象的分类器设计算法，尤其对于一类自然代价函数而言，该算法在计算上也是高效的。