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.

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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  -