Explainability, Quantified: Benchmarking XAI Techniques

Modern Machine Learning (ML) has significantly advanced various fields; yet, the challenge of understanding complex models, often referred to as the "black box problem", remains a barrier to their widespread adoption, particularly in critical domains such as medical diagnosis and financial services. Explainable AI (XAI) addresses this challenge by augmenting ML models' outputs with interpretable information to facilitate human understanding of their internal decision processes. Despite the proliferation of explainers in recent years, covering a wide range of ML tasks and explanation types, there is no consensus on what constitutes a good explanation, leaving ML practitioners without clear guidance for selecting appropriate explainers. We argue that explanation quality quantification is the enabling factor for informed explainer choices, but many proposed explanation evaluation criteria are either narrow in scope or closer to desired properties than quantifiable metrics. This paper addresses this gap by proposing a standardized set of metrics for quantitatively evaluating explanations across diverse explanation types and ML tasks. We describe in detail the metrics of Effective Compactness, Rank Quality Index and Stability, designed to assess quantitatively explanation quality for various types of explanations (attributions, counterfactuals and rules) across different ML tasks (classification, regression and anomaly detection). We then present an exhaustive benchmarking framework for tabular-based ML, comprising open datasets, trained models, and state-of-the-art explainers. For each (data, model, explainer) tuple, we measure the time of the explanation production, apply our metrics and collect the results, highlighting correlations and trade-offs between desired properties. The resulting framework allows us to quantitatively rank explainers suitable for specific ML scenarios and select the most appropriate one based on the user's requirements.