Search

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.

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.