<h2><strong>XAI Enhancing Trust in Machine Learning Models</strong></h2>
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<h2 data-start="490" data-end="510">Introduction</h2>
<p data-start="512" data-end="958">Explainable Artificial Intelligence (XAI) is rapidly evolving as an essential area of research, especially as machine learning models gain widespread adoption in high-stakes domains such as healthcare, finance, and cloud computing security. These fields rely on highly complex black-box (BB) models that offer impressive accuracy but lack interpretability, raising concerns about trust, transparency, and accountability.</p>
<p data-start="960" data-end="1472">To mitigate these concerns, surrogate models—interpretable white-box (WB) models that approximate BB models—have emerged as a popular XAI solution in cloud computing and AI applications. However, the effectiveness of surrogate models hinges on their faithfulness to the original BB models. Traditional fidelity metrics assess surrogate models based on prediction similarity, yet they fail to ensure that the reasoning paths of these models align with those of their BB counterparts.</p>
<p data-start="960" data-end="1472">To address this gap, we introduce ShapGAP, a novel metric that evaluates surrogate model faithfulness using SHAP (SHapley Additive exPlanations) values. By measuring the L2 distance between SHAP explanations of BB and WB models, ShapGAP goes beyond prediction similarity, offering a more accurate representation of surrogate model reliability in XAI applications.</p>
<h3 data-start="1878" data-end="1952"><strong>Understanding Surrogate Models and Cloud Service Management in XAI</strong></h3>
<p data-start="1954" data-end="2258">Surrogate models act as interpretable approximations of black-box models and are widely employed in XAI for cloud computing. They enable stakeholders to gain insights into complex AI decision-making processes, ensuring that automated decisions remain transparent and actionable.</p>
<p data-start="2260" data-end="2325">There are two primary types of surrogate models in XAI:</p>
<ul data-start="2327" data-end="2637">
<li data-start="2327" data-end="2497">Local Surrogates: These models provide interpretability for individual predictions by approximating the BB model’s behavior within a specific data region.</li>
<li data-start="2498" data-end="2637">Global Surrogates: These models aim to represent the overall decision-making process of the BB model across the entire dataset.</li>
</ul>
<p data-start="2639" data-end="2958">The choice between local and global surrogates depends on the specific needs of XAI applications in cloud computing. While local surrogates offer high interpretability, they may lack generalization, whereas global surrogates provide a broader perspective at the expense of local precision.</p>
<h3 data-start="2965" data-end="3045"><strong>Limitations of Traditional Fidelity Measures in Cloud Computing Security</strong></h3>
<p data-start="3047" data-end="3383">Traditional fidelity metrics, such as task accuracy and fidelity accuracy, evaluate surrogate models based on their ability to replicate the predictions of a BB model. However, these metrics can be misleading because they do not capture whether the reasoning paths of surrogate models match those of BB models.</p>
<p data-start="3385" data-end="3827">For example, in healthcare AI, a surrogate model may predict a patient’s disease risk using different symptoms than the BB model. If medical professionals rely on these predictions without understanding the true reasoning, misdiagnoses may occur. Similarly, in financial AI, an unfaithful surrogate model might approve or reject loans based on different risk factors, leading to unfair lending decisions.</p>
<p data-start="3829" data-end="3977">This highlights the necessity of robust XAI measures to ensure faithful surrogate models that truly reflect BB models’ decision logic.</p>
<h3><strong>Introducing ShapGAP for Enhanced Model Evaluation</strong></h3>
<p>To improve the evaluation of surrogate models, we propose ShapGAP, an advanced metric that measures surrogate model faithfulness by analyzing their SHAP explanations. Unlike conventional fidelity metrics, ShapGAP provides a comprehensive view of how well a surrogate model preserves the feature importance structure of the BB model.</p>
<p>The ShapGAP metric is formulated as follows:</p>
<p>\[<br />
\text{ShapGAP}(D, d) = \frac{1}{n} \sum_{i=1}^{n} d(S_{bb}(x_i), S_{wb}(x_i))<br />
\]</p>
<p>Here, \(S_{bb}(x_i)\) and \(S_{wb}(x_i)\) denote the SHAP values for the \(i\)-th instance in dataset \(D\) for the BB and WB models, respectively. The function \(d(\cdot, \cdot)\) serves as a distance function measuring the dissimilarity between the SHAP explanations.</p>
<h3><strong>Distance Measures in ShapGAP</strong></h3>
<p>ShapGAP accommodates different distance measures suited to various application requirements. Two primary distance measures utilized include:</p>
<h4><strong>1. L2 Euclidean Distance</strong></h4>
<p>This measure highlights the precise contributions of each feature in the explanation. It is notably sensitive to differences in magnitude between the SHAP values of BB and WB models.</p>
<p>\[<br />
\text{ShapGAP}_{L2}(D) = \frac{1}{n} \sum_{i=1}^{n} ||S_{bb}(x_i) &#8211; S_{wb}(x_i)||_2<br />
\]</p>
<h4>2. Cosine Distance</h4>
<p>This measure centers on the directional similarity of SHAP explanations. It facilitates the identification of surrogate models that share similar reasoning paths.</p>
<p>\[<br />
\text{ShapGAP}_{Cos}(D) = \frac{1}{n} \sum_{i=1}^{n} \left(1 &#8211; \frac{S_{bb}(x_i) \cdot S_{wb}(x_i)}{||S_{bb}(x_i)||_2 ||S_{wb}(x_i)||_2}\right)<br />
\]</p>
<p>By providing these two distance measures, ShapGAP ensures enhanced flexibility in evaluating surrogate model faithfulness while catering to diverse application needs.</p>
<h3>Experimental Validation of ShapGAP in Cloud Computing</h3>
<p data-start="5783" data-end="5954">To test ShapGAP’s performance, we conducted experiments using the Breast Cancer dataset and German Credit dataset—both commonly used in XAI research.</p>
<p data-start="5956" data-end="6378">We evaluated Logistic Regression (LR) and Decision Trees (DT) surrogate models using ShapGAP alongside traditional fidelity measures. The results revealed that LR models, despite high fidelity accuracy, exhibited higher ShapGAP values, indicating unfaithful explanations. In contrast, DT models, though slightly less accurate, had lower ShapGAP values, signifying better faithfulness.</p>
<p data-start="6380" data-end="6497">These findings reinforce the necessity of faithfulness metrics in XAI for cloud computing applications.</p>
<h3><strong>Ethical Considerations in Surrogate Explanations</strong></h3>
<p data-start="6562" data-end="6823">Unfaithful surrogate models pose serious ethical risks in XAI and cloud computing security. In healthcare AI, incorrect explanations can lead to misdiagnoses, while in financial AI, biased models can result in unfair lending decisions.</p>
<p data-start="6825" data-end="6973">Ensuring faithful explanations aligns AI with ethical principles, fostering trust, transparency, and fairness in XAI applications.</p>
<h3>Conclusions and Future Work</h3>
<p data-start="7017" data-end="7315">In this blog, we introduced ShapGAP, a novel XAI metric for evaluating surrogate model faithfulness through SHAP explanations. Our experiments demonstrate ShapGAP’s effectiveness in identifying unfaithful surrogate models, a crucial aspect of cloud computing security.</p>
<p data-start="7317" data-end="7576">However, ShapGAP has some limitations, including its <a href="https://cloudastra.co/blogs/explanation-types-in-xai">computational complexity and reliance on SHAP-based explanations</a>. Future research should explore alternative XAI strategies and optimize the efficiency of ShapGAP computations.</p>
<p data-start="7578" data-end="7783">By advancing XAI metrics, we take a significant step toward Different Ways to Understand the Act of Explaining in XAI, ensuring trustworthy and ethical AI applications in cloud computing.</p>
<p data-start="7785" data-end="7977" data-is-last-node="">At Cloudastra Technologies, we specialize in AI-driven solutions for modern businesses. Contact us to explore how XAI and cloud computing security can enhance your AI applications!</p>
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ShapGAP for Evaluating Faithful Surrogate Models in XAI

ShapGAP for Evaluating Faithful Surrogate Models in XAI

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