This systematic review reframes technical debt in AI-enabled systems through the lens of AI Trust, Risk, and Security Management (AI TRiSM). Reviewing 60 primary studies, it identifies 31 types of AI technical debt and organizes them into seven root-cause classes. The debts are mapped to 18 trust-related concerns, including six safety hazards and 12 security vulnerabilities. The authors synthesize 34 mitigation guidelines, covering prevention, detection, and reduction across the AI lifecycle, and introduce AITD-MAP to connect debt types, quality and risk impacts, and mitigation strategies.
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