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Intelligent systems increasingly influence decisions, allocate attention, generate recommendations, and mediate interactions across technical, organizational, and social environments. Yet trustworthiness is often approached through isolated interventions, such as improving model accuracy, adding post-hoc explanations, documenting datasets, or assigning nominal human oversight. This article proposes a trustworthy-by-design conceptual framework that treats trustworthiness as a system-level property requiring coordinated design and evaluation across the full intelligent-system lifecycle. The framework begins with system-purpose and risk-context definition, followed by stakeholder and responsibility mapping, data-integrity assessment, provenance and lineage review, data documentation and governance, model-reliability evaluation, robustness and uncertainty assessment, and bias and fairness review where relevant. It then connects explanation quality with human interpretability and decision usefulness, rather than assuming that technically generated explanations will necessarily support understanding. Human oversight, meaningful control, contestability, recourse, institutional accountability, traceability, auditability, monitoring, and feedback updating form additional interdependent layers. The framework’s principal contribution is to clarify how technical evidence must be connected to human decision conditions and institutional responsibility without treating any individual safeguard as sufficient. Data provenance does not establish data appropriateness, robustness does not prove safety, explanations do not guarantee interpretability, human presence does not ensure meaningful control, and governance arrangements do not independently establish accountability. The framework may support conceptual analysis, assurance-oriented evaluation, and lifecycle governance planning, but requires empirical validation, human-centered assessment, domain-specific interpretation, and continuing institutional review.

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.

Explainable artificial intelligence increasingly seeks to provide more than visualisations of feature importance or simplified descriptions of model outputs. For intelligent systems used in consequential settings, machine reasoning should be open to questioning, inspection, contextualisation, correction, and evaluation under uncertainty. This integrative review examines emerging approaches that may support this broader objective. It connects causal explainability and counterfactual reasoning with neuro-symbolic intelligence, knowledge-grounded inference, uncertainty-aware explanation, reliability evaluation, and human-centred interpretability. The review uses a structured integrative approach to compare how these paradigms represent causes, alternatives, rules, concepts, knowledge sources, inferential steps, confidence, and limitations. Causal and counterfactual approaches can support questions about mechanisms, contrasts, interventions, actionability, and recourse, but their conclusions depend on explicit assumptions, feasible alternatives, and appropriate causal evidence. Neuro-symbolic and knowledge-grounded approaches can make concepts, relations, rules, provenance, and intermediate reasoning more traceable, although symbolic structure or retrieved knowledge does not automatically ensure faithful, valid, or truthful explanation. Uncertainty-aware approaches can qualify predictions and explanations through uncertainty characterisation, calibration, confidence communication, robustness assessment, and human interpretation, but uncertainty estimates alone do not establish reliability. The synthesis indicates that no individual paradigm is sufficient. More defensible machine reasoning requires complementary evaluation of explanation fidelity, causal plausibility, knowledge validity, reasoning traceability, uncertainty communication, cognitive accessibility, contestability, and lifecycle accountability. Important research opportunities concern causal validation, feasible recourse, provenance governance, explanation uncertainty, hybrid-system evaluation, human-centred testing, and cross-paradigm assessment. The review contributes an integrated account of how machine reasoning may become not merely visible, but meaningfully open to question.

Generative artificial intelligence can produce fluent, context-sensitive, and apparently authoritative responses even when relevant knowledge is incomplete, evidence is unavailable, or generated claims are false. This creates an epistemic safety problem concerning not only what a system produces, but also what it appears to know, how its claims are supported, how uncertainty is expressed, and when answering should be limited. This article develops a conceptual safety framework organized around four operational principles: ground, qualify, verify, and abstain. The framework treats hallucination as a multidimensional risk involving fabrication, factuality failure, groundedness failure, unsupported claims, false confidence, source and context errors, reasoning errors, citation errors, overgeneralization, confabulation, speculative output, and incomplete evidence. Grounding links material claims to relevant and sufficiently reliable evidence; qualification communicates uncertainty, assumptions, scope, and evidential limitations; verification examines claims, sources, citations, and inferential consistency; and abstention enables refusal, deferral, escalation, or constrained output when evidence or system competence is insufficient. Human review, meaningful oversight, decision boundaries, failure containment, incident monitoring, and lifecycle updating operate across these principles rather than functioning as final guarantees. The main contribution is an integrated generative AI safety framework that connects output-level hallucination control with source quality, knowledge verification, uncertainty communication, review quality, response limitation, and downstream risk containment. The framework is intended to structure domain-specific assessment and future research. It does not independently establish factual reliability, ethical sufficiency, legal compliance, operational safety, certification, or deployment readiness.