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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.

Foundation models are increasingly embedded in information access, content production, analysis, communication, and decision-support processes. Their broad capabilities have intensified interest in alignment, yet alignment alone cannot establish responsible use. A model may appear responsive to instructions or preferences while remaining opaque, weakly grounded, unreliable under changing conditions, overconfident, difficult to contest, or insufficiently governed. This article proposes a conceptual governance and technical safeguard framework for responsible foundation models. The framework treats foundation models as components of decision infrastructure situated within socio-technical and institutional environments rather than as isolated predictive tools. It integrates eight interdependent areas: model and alignment safeguards; explainability and interpretability; knowledge grounding and provenance; reliability, uncertainty, and hallucination containment; human control and escalation; institutional accountability; auditability and documentation; and lifecycle monitoring and governance updating. Explainability requirements are differentiated from interpretability, transparency, explanation usefulness, and human understanding, while knowledge grounding is separated from factuality, truthfulness, source quality, and provenance. Reliability is addressed through calibration, robustness assessment, boundary testing, uncertainty communication, failure containment, and continuing monitoring. Human oversight is defined through authority, competence, review capacity, escalation pathways, contestability, and recourse rather than nominal human presence. Institutional responsibility is connected to role allocation, evidence trails, documentation, review authority, and consequences. The principal contribution is an integrated framework that clarifies how technical and governance safeguards may support responsible foundation-model assessment without treating alignment, explanation, grounding, testing, oversight, or auditing as independently sufficient. The framework requires empirical validation, domain-specific evaluation, human-centred assessment, institutional governance, and residual-risk management before it can inform consequential operational decisions.