The Role of Randomness and Noise in Strategic Classification

Machine learning algorithms are increasingly being used to make decisions about the individuals in various areas such as university admissions, employment, health, etc. As the individuals gain information about the algorithms being used, they have an incentive to adapt their data so as to be classified desirably. For example, if a student is aware that a university heavily weighs SAT score in their admission process, she will be motivated to achieve a higher SAT score either through extensive test preparation or multiple tries. Such efforts by the students might not change their probability of being successful at the university, but are enough to fool the admissions' process. Therefore, under such "strategic manipulation" of one's data, the predictive power of the decisions are bound to decrease. One way to prevent such manipulation is by keeping the classification algorithms a secret, but this is not a practical solution to the problem, as some information is bound to leak over time and the transparency of these algorithms is a growing social concern. Thus, this motivates the study of algorithms that are optimal under "strategic manipulation". The problem of gaming in the context of classification algorithms is a well known problem and is increasingly gaining researchers' attention, for example, [HMPW16, ALB16, HIV19, MMDH19, DRS