Beat the Machine: A Gamified Approach to Identifying Systematic Errors in Predictive Models
- DOI
- 10.2991/978-94-6463-787-8_54How to use a DOI?
- Keywords
- machine learning evaluation; model robustness; unknown unknowns; gamified testing; human-in-the-loop; adversarial examples
- Abstract
In predictive modeling, systematic errors—often referred to as unknown unknowns—can lead to critical failures, especially when models are overconfident in their incorrect predictions. This paper presents a gamified framework, Beat the Machine (BTM), which incentivizes the identification of such systematic misclassifications. BTM rewards participants based on error severity, emphasizing high-confidence failures that conventional quality assurance methods tend to overlook. The pro- posed approach is benchmarked against the widely used Stratified Random Sampling method (for a detailed discussion, see Cochran’s Sampling Techniques [4]) and demonstrates superior performance in uncovering both the frequency and severity of misclassifications, as supported by im- proved AUC metrics [6]. The results underscore the potential of gamified error detection to enhance model robustness and offer a new paradigm for systematic quality assurance in machine learning applications.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Akash Kota Raju PY - 2025 DA - 2025/07/17 TI - Beat the Machine: A Gamified Approach to Identifying Systematic Errors in Predictive Models BT - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025) PB - Atlantis Press SP - 721 EP - 735 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-787-8_54 DO - 10.2991/978-94-6463-787-8_54 ID - Raju2025 ER -