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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.

We introduce and execute a transparent machine learning classification model designed for Explainable AI (XAI) that leverages expressive Boolean formulas. Potential domains of use include credit risk evaluation and medical diagnostics. The system utilizes a Boolean formula to establish a rule with adjustable complexity (or interpretability) for categorizing input data. This formula accommodates any operator applicable to single or multiple Boolean variables, offering superior expressivity relative to more rigid rule- and tree-based methodologies. Native local optimization methods are used to train the classifier, allowing for an efficient search of the feasible formula space. Fast Integer Linear Programming (ILP) or Quadratic Unconstrained Binary Optimization (QUBO) solvers—potentially enhanced by custom hardware or quantum systems—can identify shallow rules. By executing non-local moves that optimize across subtrees of the complete Boolean formula, we merge the expressivity and efficiency of the native local optimizer with the rapid processing capabilities of these devices. We present comprehensive numerical benchmarking data against multiple baseline models using recognized public datasets. The findings indicate that the native local rule classifier performs competitively with alternative models. Incorporating non-local moves yields comparable outcomes with a reduced number of iterations. Consequently, deploying specialized or quantum hardware could provide substantial acceleration by rapidly generating non-local moves.

Artificial intelligence increasingly informs consequential decisions, yet high predictive performance alone does not establish that an intelligent system is worthy of trust. This systematic review examines trustworthy AI as a multidimensional, socio-technical, and lifecycle-dependent research domain. It synthesises selected peer-reviewed journal evidence addressing transparency, interpretability, explainability, robustness, safety, privacy, security, fairness, accountability, governance, and meaningful human oversight. The review applies predefined questions, eligibility principles, trustworthiness-dimension mapping, claim-level evidence extraction, and cross-dimensional synthesis without undertaking meta-analysis or inventing quantitative screening results. The evidence indicates that transparency and explanation must be assessed in relation to fidelity, stability, completeness, usefulness, stakeholder needs, contestability, recourse, and the practical authority of human overseers. Robustness and safety require evaluation under adversarial conditions, uncertainty, context shift, system failure, and residual risk, while privacy-preserving approaches must still be examined for leakage, cybersecurity vulnerabilities, and governance limitations. Fairness depends on context-sensitive definitions, bias identification, distributional consequences, documentation, responsibility allocation, auditability, and institutional mechanisms for correction. Cross-dimensional analysis reveals persistent tensions between transparency and privacy, explainability and security, automation and human control, and performance and fairness. Important gaps remain in multidimensional evaluation, realistic human-oversight studies, explanation-usefulness validation, lifecycle monitoring, accountability operationalisation, stakeholder participation, and foundation-model assurance. The review contributes an integrated evidence synthesis showing that AI worthiness of trust should be supported through coordinated technical validation, human-centred assessment, institutional governance, and continuing monitoring rather than inferred from any isolated property.

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.

Explainable artificial intelligence increasingly offers diverse ways to communicate how intelligent systems operate, yet explanation selection is still frequently driven by method availability or model compatibility rather than stakeholder need. This article develops a stakeholder-oriented taxonomy for determining which explanation type may be appropriate for whom, at which decision moment, and for what purpose. Explanation is treated as a multiform system capability encompassing different scopes, reasoning functions, presentation modes, interaction patterns, and quality requirements. The taxonomy distinguishes global explanations concerned with model-level behaviour from local explanations directed at individual predictions, while clarifying the roles and limitations of feature attribution, saliency, surrogate models, rule extraction, transparency, fidelity, stability, and completeness. It further differentiates counterfactual, contrastive, and causal explanations according to their contributions to alternative reasoning, actionability, contestability, recourse, and accountability. Example-based, case-based, prototype, rule-based, natural-language, interactive, dialogue-based, adaptive, and personalized explanations are positioned according to user expertise, cognitive accessibility, task demands, and interaction context. The proposed taxonomy connects explanation families with stakeholder roles, pre-decision, during-decision, and post-decision timing, explanation purpose, decision consequence, uncertainty, trust calibration, human oversight, and accountability requirements. Its central contribution is a structured selection logic that moves beyond technical method categories toward stakeholder–purpose–timing–risk alignment. The taxonomy can guide explanation design and evaluation but does not independently establish understanding, fairness, recourse, accountability, legal sufficiency, trustworthiness, or deployment readiness. Stakeholder-specific and decision-context-specific validation therefore remains necessary.