Human-in-the-Loop AI for Cheating Ring Detection
This addresses security concerns for online exam platforms, but it appears incremental as it applies existing human-in-the-loop methods to a specific domain.
The paper tackles the problem of cheating rings in online exams by introducing a human-in-the-loop AI detection system, with results including performance and fairness evaluations to mitigate risks.
Online exams have become popular in recent years due to their accessibility. However, some concerns have been raised about the security of the online exams, particularly in the context of professional cheating services aiding malicious test takers in passing exams, forming so-called "cheating rings". In this paper, we introduce a human-in-the-loop AI cheating ring detection system designed to detect and deter these cheating rings. We outline the underlying logic of this human-in-the-loop AI system, exploring its design principles tailored to achieve its objectives of detecting cheaters. Moreover, we illustrate the methodologies used to evaluate its performance and fairness, aiming to mitigate the unintended risks associated with the AI system. The design and development of the system adhere to Responsible AI (RAI) standards, ensuring that ethical considerations are integrated throughout the entire development process.