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Explainability has become central to discussions of trustworthy and responsible intelligent systems, yet it is frequently reduced to the technical production of information about model structure, features, rules, or predictions. This critical review examines why opening a computational black box is not equivalent to producing an explanation that people can understand or use responsibly. The review synthesises conceptual, technical, human-centred, decisional, and accountability-oriented scholarship to evaluate the shift from model interpretability toward meaningful explanation. It distinguishes technical properties such as transparency, fidelity, plausibility, completeness, stability, and local or global scope from human outcomes such as comprehension, cognitive accessibility, mental-model development, decision usefulness, calibrated trust, and appropriate reliance. It further examines how explanations may produce overreliance, underreliance, automation bias, false reassurance, cognitive overload, or explanation mismatch when their content and presentation are not aligned with users, tasks, uncertainty, and decision consequences. From an accountability perspective, explanations may contribute to auditability, traceability, contestability, and recourse, but they cannot independently assign responsibility, provide institutional authority, or guarantee effective remedies. The main contribution is a human-centred critical synthesis that treats explainability as a socio-technical achievement rather than a model output. Meaningful explanation requires technical credibility, cognitive accessibility, contextual relevance, decision-sensitive evaluation, uncertainty communication, opportunities for challenge, and governance mechanisms capable of acting on explanatory evidence. The review therefore rejects universal explanation-sufficiency claims and identifies conditions under which explanations may assist understanding and responsible decision use without being treated as proof of trustworthiness, accountability, fairness, safety, or deployment readiness.

Foundation models have expanded the scope of artificial intelligence by supporting language generation, multimodal reasoning, knowledge-intensive assistance, decision support, and adaptable downstream applications. Their broad capabilities, however, do not establish that their outputs, explanations, internal processes, or deployment arrangements are trustworthy. This scoping review maps the research landscape concerning foundation-model trustworthiness across interpretability, explanation capabilities, hallucination, uncertainty, reliability, alignment, evaluation, and human oversight. The review uses structured search concepts, eligibility principles, evidence extraction, and dimension-based mapping to examine how peer-reviewed scholarship characterizes foundation models as general-purpose intelligent systems and how trustworthiness evidence is distributed across technical, human-centered, organizational, and governance domains. The synthesis indicates that interpretability methods can expose selected behavioral or representational features but do not independently demonstrate explanation fidelity, usefulness, or safety. Hallucination and factuality research identifies persistent risks involving fabrication, unsupported claims, weak grounding, and persuasive misinformation, while uncertainty methods remain constrained by calibration, context shift, and user-communication challenges. Alignment and post-training techniques may shape preferred behaviors but retain risks associated with proxy objectives, preference representation, and residual failure. Benchmarking, human evaluation, stress testing, and oversight provide partial evidence, yet their value depends on construct validity, domain relevance, intervention capacity, accountability, and lifecycle monitoring. The research landscape is therefore fragmented across disciplines and frequently model-centred rather than socio-technical. Key priorities include stronger foundation-model interpretability validation, consistent hallucination measurement, usable uncertainty communication, externally valid evaluation, meaningful oversight testing, multimodal analysis, and lifecycle governance. The review contributes an integrated evidence map while emphasizing that no single trustworthiness dimension certifies safety, reliability, accountability, or deployment readiness.