## 2 Background and Related Work
### AI/ML Competition Platforms
#### 2.4.4 Challenges and Considerations in AI Competitions  
Despite their many advantages, AI competition platforms also face important challenges. A key concern is ensuring fairness and transparency throughout the evaluation process. Bias in datasets, inconsistencies in evaluation metrics, and limited mechanisms for reproducibility can distort results and inadvertently favor participants with greater computational resources or prior experience [96]. The widespread use of public leaderboards, while offering timely feedback, can sometimes encourage \"leaderboard chasing\" behaviors--where participants optimize narrowly for leaderboard position at the expense of model generalizability. These practices can lead to both technical and ethical debt over time.  
Another persistent issue relates to resource accessibility. On platforms like Kaggle, access to advanced computing features such as Graphic Processing Unit (GPU) acceleration is often restricted to paid users or those with higher platform rankings, creating entry barriers for newcomers and participants from under-resourced institutions [6]. Such disparities risk reinforcing existing inequities in AI education and participation. Furthermore, the competitive nature of these platforms--often characterized by tight deadlines and performance pressure--can incentivize shortcuts and self-admitted technical debt (SATD),ultimately undermining the maintainability and long-term robustness of the developed systems [80,82].  
Beyond these common limitations, some AI competition environments may also lack structured mechanisms for reflecting on technical decision-making. For instance, early versions of game-based environments such as RLGame exposed students to AI development under time pressure, but often without explicit prompts to document or justify architectural trade-offs--an issue that, if unaddressed, leads to design and documentation debt [14]. Similarly, in the Geometry Friends collaborative platform, participants often encounter strategic coordination challenges that bring to the surface fragile or inflexible planning components, particularly when agent behavior is hard-coded or tightly coupled [104].  
The GBG framework highlights another layer of challenge: while it encourages experimentation with diverse agents across games, it also requires careful modularization and consistent evaluation practices to avoid reinforcing non-generalizable design patterns and evaluation bias [8]. In all these cases, the absence of integrated support for version control, modular pipelines, or structured reflection may hinder participants from recognizing the long-term consequences of their design decisions.  
These issues highlight the need for AI competitions to be designed with inclusive infrastructure, blind evaluation phases, and opportunities for reflection during development. Embedding clear criteria for fairness, explainability, and sustainability within competition guidelines can lead to not only better-performing systems but also more ethically aligned and responsible AI practices. Platforms that emphasize reproducibility, transparent scoring mechanisms, and awareness of technical debt offer participants a more valuable and reflective learning experience [9,10,16].