Trust-Aware Smart Algorithms for Open Digital Service Ecosystems: A Structured Review

Open digital service ecosystems are reshaping how users discover and act on services across commerce, tourism, finance, education, accessibility, and public compliance. However, the literature on underlying algorithms remains fragmented, some prioritize ranking accuracy, others focus on adoption, and a smaller body examines explainability, fairness, privacy, accessibility, and governance. This paper presents a structured review of 74 studies (55 from 2017 to March 2026, plus 19 foundational works) mapping the transition from closed, accuracy-centered recommenders to open, trust-aware, multi-objective smart algorithms. Hybrid and context-aware architectures remain dominant, but recent work increasingly incorporates conversational interfaces, human-in-the-loop control, privacy constraints, and fairness-aware reranking. Five recurring gaps emerge: data fragmentation, weak explanation quality, limited accessibility auditing, poor integration of institutional rules, and weak cross-sector transferability. Trust is best understood as a composite property involving data provenance, procedural transparency, contextual fit, user control, and reliable escalation paths. Based on these findings, the paper proposes a six-layer design model (data interoperability, contextual risk profiling, hybrid inference, governance-aware reranking, explanation interfaces, and human oversight). Contributions: (1) a PRISMA-informed review protocol; (2) a taxonomic classification of five smart algorithm families; (3) a composite trust framework operationalized from Mayer et al. (1995); (4) quantitative comparisons of trust-related metrics; (5) mathematical formulations for trust-aware reranking and explanation generation; (6) a benchmark against NIST AI RMF and EU AI Act; and (7) a six-layer architectural model with implementation examples.