\begin{abstract}

Explanations of Local Interpretable Model-agnostic Explanations (LIME) are often inconsistent across different runs making them unreliable for eXplainable AI (XAI). The inconsistency stems from sign flips and variability in ranks of the segments for each different run. We propose a Bayesian Regularization approach to reduce sign flips, which in turn stabilizes feature rankings and ensures significantly higher consistency in explanations. The proposed approach enforces sparsity by incorporating a Sign Entropy prior on the coefficient distribution and dynamically eliminates features during optimization. Our results demonstrate that the explanations from the proposed method exhibit significantly better consistency and fidelity than LIME (and its earlier variants). Further, our approach exhibits comparable consistency and fidelity with a significantly lower execution time than the latest LIME variant, i.e., SLICE. 

%The consistency of explanations are demonstrated to be consistent using the experiments on two different datasets and two different deep learning models along with statistical tests to validate the approach.

% This eliminates inconsistent features (superpixels in LIME for image) from the explanations, thereby mitigating sign flips and stabilizing the importance ranks. 
 
\end{abstract}