# Synthesizing Explainability Across Multiple ML Models for Structured Data
## 2 Background and Related Work  
While rapid advancement in the efficacy of ML systems over the past decade has been notable, the adoption of increasingly complex models, particularly \"black-box\" techniques such as ensembles or Deep Neural Networks, has been impeded by their lack of inherent interpretability [14]. This challenge has propelled the field of XAI, which is now widely acknowledged as a crucial feature for the practical deployment of AI models and is becoming a prerequisite, even enforced by law in some regions [7]. XAI is also intertwined with the broader concept of Responsible Artificial Intelligence (RAI), which emphasizes principles such as fairness and accountability alongside model explainability [7]. A solid understanding of the underlying concepts, processes, and challenges of ML and Deep Learning (DL)is required for their effective implementation [15]. We review the existing literature on XAI methods and taxonomies, examine current practices, and highlight key challenges relevant to achieving reliable explanations from complex and potentially multiple ML models, addressing our research questions. Research in XAI has introduced many methods, and several surveys now group these techniques using different criteria [4; 6; 7]. A primary distinction is often made between transparent or intrinsically interpretable models and post-hoc explainability techniques applied to opaque or black-box systems [4; 7]. Taxonies also categorize methods by the scope of interpretability (local, explaining individual predictions, versus global, explaining overall model behavior) [4] and dependency on the model (model-agnostic techniques applicable to any ML model versus model-specific techniques tailored to certain architectures) [4; 7]. Popular post-hoc model-agnostic techniques include LIME and SHAP, which aim to provide insights into model predictions, often by estimating the importance or contribution of input features [7]. While a wide array of techniques exists, a standardized framework for evaluating the quality and effectiveness of the generated explanations is still missing [13]. Evaluation often involves quantitative metrics, user-focused assessments, or clinical validation in domain-specific applications [6; 13]. Despite the advancements in XAI methodologies, significant challenges remain, particularly when applying and synthesizing explanations across multiple models or complex ensemble structures. One notable issue is the inconsistency or \"disagreement problem\" that can arise between explanations provided by different XAI methods or even the same method applied to slightly different models or datasets [13; 14]. For instance, the factor contribution values and rankings provided by methods like Kernel SHAP can differ across various models for the same dataset [1]. This variability relates to the challenge of achieving stability of explanations, specifically the consistency of the set of features identified as important [11]. The differing importance ranks for factors across models highlight that a single explanation may not reliably reflect the true underlying importance. Addressing these inconsistencies and ensuring the stability and reliability of identified important features is crucial for fostering trust in AI systems [1]. The complexity of implementing taxonomies across diverse methods also presents a challenge [4]. The ability to synthesize information from potentially multiple models is relevant, especially considering if an ensemble approach can yield more reliable predictors than single models. Over time, the evolution of explainability has followed a clear progression: initially, most machine-learning models were treated as full black boxes. Gradually, distinctions emerged as some models offered intrinsic transparency. Next came ranking feature importance within individual models, followed by attempts to compare these rankings across models.