BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations (2012.03058v5)

Abstract: Given the pressing need for assuring algorithmic transparency, Explainable AI (XAI) has emerged as one of the key areas of AI research. In this paper, we develop a novel Bayesian extension to the LIME framework, one of the most widely used approaches in XAI -- which we call BayLIME. Compared to LIME, BayLIME exploits prior knowledge and Bayesian reasoning to improve both the consistency in repeated explanations of a single prediction and the robustness to kernel settings. BayLIME also exhibits better explanation fidelity than the state-of-the-art (LIME, SHAP and GradCAM) by its ability to integrate prior knowledge from, e.g., a variety of other XAI techniques, as well as verification and validation (V&V) methods. We demonstrate the desirable properties of BayLIME through both theoretical analysis and extensive experiments.

Bayesian Local Interpretable Model-Agnostic Explanations: A Technical Overview

The burgeoning field of Explainable AI (XAI) has sought to address the opacity of AI models, particularly deep learning models, by developing methodologies that render their decisions transparent and interpretable. Among the more prominent framework within XAI is the Local Interpretable Model-agnostic Explanations (LIME). This paper introduces a Bayesian augmentation to LIME, termed BayLIME, which leverages prior knowledge and Bayesian principles to enhance the consistency, robustness, and fidelity of model explanations.

Key Innovations

The paper identifies significant limitations within LIME, notably concerning its consistency in providing identical predictions upon repeated trials, its sensitivity to kernel settings, and its sometimes suboptimal fidelity to the true underlying mechanism of the AI model. BayLIME addresses these issues by introducing a Bayesian framework that incorporates prior distributions to yield conditioned explanations.

1. Consistency in Explanations: Traditional LIME is prone to produce variably differing explanations for the same instance during repeated runs due to the randomness in generated perturbed samples. By integrating prior knowledge through Bayesian methods, BayLIME effectively stabilizes this variability.
2. Robustness to Kernel Settings: LIME's explanations can shift significantly with different kernel width choices, impacting the notion of an instance's neighborhood. BayLIME, by contrast, diminishes this sensitivity by utilizing priors independent of kernel settings, thus ensuring robustness irrespective of kernel parameter selections.
3. Fidelity of Explanations: The capability of an explanation to accurately mirror the AI system's decision-making process—termed fidelity—is critical. BayLIME's Bayesian mechanism, by incorporating multilayered and potentially diverse information, surpasses traditional LIME and other competitive techniques like SHAP and GradCAM, especially in settings where full or partial priors can be defined.

Experimental Findings

The empirical prowess of BayLIME is demonstrated through varied datasets, including tabular data and CNNs trained on ImageNet and GTSRB. The improved explanatory consistency is quantitatively measured using Kendall’s W, establishing BayLIME’s superior reliability across different sample sizes. Furthermore, robustness metrics substantiate BayLIME’s decreased sensitivity to kernel parameters, solidifying its application versatility. Finally, fidelity measurements using deletion and insertion metrics illustrate BayLIME's enhanced alignment with the actual decision logic of the AI model.

Implications and Future Work

The implications of BayLIME are substantial for assurance in AI systems deployed in sensitive domains such as healthcare and autonomous systems. Its Bayesian architecture provides a template for incorporating prior domain knowledge or insights from other XAI methods, paving the path for hybrid explanations that combine theoretical rigor with empirical evidence.

Future trajectories could explore the domain-specific derivation of prior distributions to further enhance BayLIME’s applicability and accuracy. Additionally, expanding the types of Bayesian priors and the corresponding elicitation processes may invariably enrich the explanation accuracy across varied applications. Moreover, addressing the challenge of integrating priors while maintaining computational efficiency remains an essential avenue for research.

BayLIME stands as an intriguing augmentation of LIME, offering not only methodological advancements but also practical enhancements in trust and transparency, thereby reinforcing the foundational goals of XAI.