# Synthesizing Explainability Across Multiple ML Models for Structured Data
## 1 Introduction  
The pervasive integration of Artificial Intelligence (AI) and Machine Learning (ML) imposes increased interpretability and transparency, especially as these systems influence significant decisions [1, 2, 3]. The demand for explainable algorithms stems from diverse stakeholders, including affected individuals, regulators, domain experts, and developers, each with potentially different explanation requirements [4]. This concern is further echoed in safety-critical contexts, where AI introduces non-deterministic behavior that traditional engineering methods struggle to assure [5]. As a result, reliable and transparent interpretability techniques, such as structured methods for aggregating explanations, are essential to reinforce trust and validate behavior in complex, real-world systems. The lack of understanding surrounding machine-learning system decisions presents a disadvantagein various domains, notably in healthcare and autonomous systems [2]. Furthermore, new regulations and the requirements of highly regulated fields mandate the suitability and verifiability of algorithmic decisions [3]. Consequently, there is a growing demand for the ability to question, comprehend, and trust machine-learning systems, making interpretability indispensable. Explainable AI (XAI) research aims to address the black box problem by developing methods to make AI decisions understandable [6]. The literature distinguishes between inherently interpretable models and complex models that require post hoc explanation techniques to reveal their inner workings [7]. While the latter often achieve superior accuracy, they rely on external methods to provide meaningful insights into their predictions [8]. Popular post hoc techniques include Local Interpretable Model-agnostic Explanations (LIME) [9] and SHapley Additive exPlanations (SHAP) [10], which aim to explain predictions by quantifying the contribution [8; 11] or importance [1] of input features, respectively. A significant challenge in XAI research and practice is the inconsistency and disagreement observed between explanations generated by different state-of-the-art methods for the same model prediction [12]. Practitioners frequently encounter these disagreements, often resorting to ad hoc heuristics or expressing uncertainty regarding how to resolve them reliably. This highlights a critical gap and the potential for practitioners to rely on misleading explanations [12]. Evaluating the quality of explanations is a critical but often missing step in XAI research, with a lack of consensus on standardized metrics and practices [6; 13]. Evaluation approaches can include functionality-grounded (objective metrics), application-grounded (domain-specific tasks), and human-grounded (user studies) assessments [13]. We investigate the problem of explanation disagreement, particularly in the context of feature-importance ranking, and propose a methodology to derive more stable and reliable factor importance measures by aggregating explanations from multiple models. This research addresses two key questions: whether brute-force modeling using a large number of models can improve the reliability of identified predictors and whether aggregating explanations across those models serves as an effective mechanism to achieve that goal. We present a cross-ensemble feature ranking methodology leveraging the WISFC procedure and the AGREE method [14]. The approach aggregates local explanations from an ensemble of diverse machine-learning models to produce a more robust aggregate feature-importance vector. The methodology is evaluated using several classification and regression datasets, including a non-objective-oriented healthcare dataset, demonstrating its ability to provide stable and reliable feature rankings compared to single explanation methods [1]. This is particularly valuable for obtaining dependable insights from challenging datasets, such as those that may be small and imbalanced.