Optimal Adversarial Testing: Extracting Honest Test Results from Dishonest Test Takers
Cryptography and SecurityArtificial IntelligenceComputer Science and Game Theory
Summary
The authors address the problem of testing people or objects when some might cheat, especially with AI tools making cheating easier. They propose smart testing plans that include re-testing certain individuals with extra security steps to get accurate results despite cheating. Their method uses a step-by-step mathematical approach called dynamic programming to find the best strategy for dealing with dishonest test takers.
Authors
Owen Cox, April Xu, Weiyu Xu
Abstract
In applications, it is often required to test objects or people to determine their qualities in terms of certain metrics. However, besides being naturally noisy, the test results can be corrupted by adversarial behaviors of objects or people being tested (test takers). For example, dishonest test takers can cheat in the exams to distort the test results. With the development of AI technologies, such distortions driven by cheating using AI technologies are becoming more commonplace and severe. In this paper, we propose optimal testing strategies which can still recover needed test results even if there are cheaters polluting the results. The proposed testing strategies will optimally re-test selected group of test takers using different testing security measures. We determine the optimal testing strategies using a dynamic programming method.