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In recent years, considerable attention has been directed toward clarifying complex, opaque systems, commonly referred to as black-box models, including deep neural networks and random forests. So-called model-agnostic techniques generally approximate the outputs of these non-transparent systems using an interpretable architecture, ignoring the internal mechanisms of the primary black-box system itself. Consequently, it remains an important inquiry whether the straightforward training of transparent, white-box architectures ought to be favored over the post-hoc approximation of these opaque models. This study presents the findings of an empirical investigation comparing post-hoc explanatory methods against inherently interpretable architectures across multiple datasets, focusing on both rule-based and feature-based frameworks. The empirical outcomes indicate that directly trained transparent models frequently approximate black-box models with an accuracy that matches or exceeds that of post-hoc surrogate models, despite lacking direct access to the underlying black-box structure.

Machine learning-driven decision support tools ought to assist users in detecting prospective advantages and risks. Widespread model-independent explanation frameworks can pinpoint elements underpinning specific forecasts—addressing queries like "What factors affect sales?" or "Why did sales decline?"—yet fail to indicate the actionable steps an individual can or should take to achieve a more favorable result. Although counterfactual explanation methods target actionable interventions and occasionally account for feasibility, they generally overlook viability for real-time systems like interactive question-answering platforms. In this study, we bridge this divide by proposing a unique model-agnostic technique that details concrete, viable adjustments capable of altering the predictions of a sophisticated Black Box AI framework for a specific instance; furthermore, we evaluate its practical utility by evaluating its computational speed in real time alongside its capacity to discover executable modifications. The developed approach leverages the target instance to produce high-precision explanations, subsequently employing a secondary mechanism to identify achievable minimally-contrastive counterfactual explanations (AMCC) by restricting the exploration space exclusively to modifications that adhere to domain-specific restrictions. Utilizing a well-established dataset, we assessed the classification procedure to determine both the success rate and the computational duration necessary to pinpoint valid counterfactual alternatives. Within the context of a classifier maintaining 90% accuracy, our proposed algorithm successfully extracted AMCC explanations in 47% of instances (38 of 81), requiring an average generation time of 80 ms. These outcomes confirm the efficiency of the algorithm in rapidly generating AMCC explanations, establishing its appropriateness for real-time deployments. The AMCC approach improves the interpretability of obscure Black Box AI models, thereby assisting stakeholders in analyzing corrective techniques or evaluating prospective consequences.

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.