  Advances in AI have led to new types of technical debt in software
engineering projects. AI-based competition platforms face challenges due to
rapid prototyping and a lack of adherence to software engineering principles by
participants, resulting in technical debt. Additionally, organizers often lack
methods to evaluate platform quality, impacting sustainability and
maintainability. In this research, we identify and categorize types of
technical debt in AI systems through a scoping review. We develop a
questionnaire for assessing technical debt in AI competition platforms,
categorizing debt into various types, such as algorithm, architectural, code,
configuration, data etc. We introduce Accessibility Debt, specific to AI
competition platforms, highlighting challenges participants face due to
inadequate platform usability. Our framework for managing technical debt aims
to improve the sustainability and effectiveness of these platforms, providing
tools for researchers, organizers, and participants.
