## 2 Background and Related Work
### Technical Debt in AI/ML Systems
#### 2.1.13 Process Debt in AI/ML Systems  
Process debt in AI/ML systems often arises from unstructured development workflows, insufficient lifecycle governance, and weak coordination between AI experts and software engineers [70; 71; 72]. These shortcomings frequently lead to issues such as inconsistent versioning of data and models, manual and error-prone handovers, and limited traceability across the development pipeline. Addressing process debt involves embracing MLOps principles [38], applying structured AI development lifecycles like CRISP-ML(Q) [49], integrating monitoring and automation tools, and fostering close interdisciplinary collaboration throughout the AI engineering process. Documentation debt in AI/ML Systems  
Documentation debt arises when AI/ML systems suffer from incomplete, inconsistent, or outdated documentation practices [71; 73]. Fragmented documentation reduces maintainability, hinders collaboration, and complicates system reproducibility [74; 75]. The lack of structured model cards, pipeline documentation, and data provenance records exacerbates the issue [74]. To address documentation debt, standardized documentation frameworks, integration of documentation into development workflows, and regular audits are essential [73; 74]. People and social debt in AI/ML systems arises when ineffective communication, unclear roles, or weak knowledge sharing hinder collaboration among teams and stakeholders [76; 77; 78]. These issues can lead to misaligned expectations, inconsistent coding practices, and a lack of shared understanding about system goals--factors that often contribute to the silent accumulation of technical debt. Recent empirical studies have also highlighted how communication breakdowns and organizational silos significantly hinder ML collaboration and contribute to hidden technical debt [71]. Mitigating this form of debt requires strengthening interdisciplinary collaboration, clarifying responsibilities, adopting structured communication protocols, and ensuring that social and technical aspects of development are aligned throughout the lifecycle [77; 78]. Ethics Debt in AI/ML Systems  
Ethics debt in AI systems arises when foundational ethical principles--such as fairness, transparency, and accountability--are not systematically integrated throughout the development lifecycle [79; 80]. Overlooking these dimensions can lead to biased or discriminatory outcomes, unintended societal consequences, and exposure to legal or regulatory risks. Proactively managing ethics debt involves embedding responsible AI practices from the design phase onward, adopting ethics-by-design methodologies, and establishing continuous auditing mechanisms to monitor compliance and impact [26; 81; 82]. Self-Admitted Technical Debt (SATD) in AI/ML Systems  
Self-Admitted Technical Debt (SATD) refers to cases where developers explicitly acknowledge technical limitations, incomplete implementations, or intentional workarounds in comments, documentation, or tracking systems [63; 83; 84]. In AI systems, SATD commonly appears in the form of simplified evaluation procedures, incomplete data preprocessing, or experimental code segments that were never fully integrated [29]. Addressing SATD effectively involves using automated tools for early detection, incorporating structured refactoring routines, and embedding SATD management into the broader governance practices of AI development projects. Defect debt in AI/ML systems arises from unresolved anomalies, latent bugs, or hidden defects that accumulate over time, affecting system reliability and performance [7; 29; 30]. Due to model non-determinism and dependency complexity, defect diagnosis in AI systems is challenging. Addressing defect debt requires systematic defect tracking, rigorous validation and testing pipelines, continuous monitoring of model behavior, and collaborative defect resolution across development and data engineering teams [7; 30].