# GlassBoost: A Lightweight and Explainable Classification Framework for Tabular Datasets
## 1 Introduction  
This paper introduces a novel, computationally efficient explainable artificial intelligence (XAI) approach called GlassBoost that has performance comparable to complex black-box models and is applicable to tabular datasets. XAI has emerged as a critical research area dedicated to enhancing the interpretability of AI systems. The need for transparent and human-interpretable models has become more pressing as AI becomes increasingly integral to high-stakes industries. This is particularly concerning for opaque or 'black-box' machine learning (ML) models, where the relationship between model input and output is often incomprehensible to end users. While powerful in their predictive capabilities, these complex models create significant challenges for stakeholders who mustunderstand how decisions are made and trust these systems. However, the importance of XAI extends far beyond simply building trust; it serves as an essential tool that enables AI developers to demonstrate compliance with regulatory policies and accountability standards [1]. Regulatory bodies worldwide have begun to recognise this need. For example, through its AI Act, the European Union has already prohibited malicious AI practices, and fines of up to EUR 35 million can be levied for breach events [2]. This landmark legislation underscores the growing regulatory emphasis on AI transparency and accountability. XAI is an essential component of striking a balance between AI effectiveness and ethical and trustworthy practices. Amid this growing interest, several comprehensive reviews have emerged, exploring foundational approaches and recent developments in XAI [1; 3; 4; 5]. The choice of ML algorithm for any application depends on the underlying data type. Tabular data remain one of the most dominant formats in real-world machine learning applications, particularly within domains such as finance, healthcare, and cybersecurity. Despite the increasing popularity of unstructured data types (e.g., images, audio, and text), tabular datasets continue to account for a significant proportion of practical use cases. For instance, in the synthetic data generation industry, tabular data held the largest market share (38.8%) in 2023, surpassing image and text data [6]. Recent studies show that over 65% of datasets on Google Dataset Search contain tabular files [7]. An analysis of the OpenML platform, a widely used repository for machine learning benchmarking, reveals that approximately 76% of datasets contain fewer than 10,000 instances, highlighting the prevalence of small-to-medium-sized tabular datasets [8]. Additionally, empirical studies emphasise that in many real-world scenarios, classical machine learning models trained on tabular data often outperform deep learning approaches tailored to unstructured formats [9]. These findings underscore the importance of developing explainable methods designed explicitly for tabular data. While there is no universally agreed-upon definition of explainability, several general definitions and categorisations provide a conceptual foundation for the term [10]. Explainability approaches are diverse, and when applied to specific tasks, their scope often narrows due to the nature of the problem and the type of available data. Consequently, explainability tailored to a specific model or type of data typically becomes more constrained than its broader, more general concepts and applications. Nonetheless, explainability continues to evolve beyond traditional and well-known approaches, encouraging the adoption of innovative techniques that reveal the hidden mechanisms of complex machine learning models. The objective of this paper is to derive, present, and evaluate an XAI approach that is computationally efficient and achieves a performance comparable with more complex black-box approaches. We introduce the result of this work, GlassBoost, a novel explainable approach that is broadly applicable to tabular datasets.