exhibit A.

The increasing integration of artificial intelligence into critical decision-making domains such as healthcare, finance, and autonomous systems has intensified the demand for transparency and interpretability in machine learning models. Many state-of-the-art models, particularly those based on deep learning architectures, are often regarded as opaque or "black boxes" due to their complex internal representations and non-linear decision boundaries. This opacity poses significant challenges when these models are deployed in environments where understanding the rationale behind predictions is essential for safety, accountability, and user trust [1][2]. Explainable AI (XAI) has emerged as a response to these challenges by providing methodologies that aim to make the behavior of complex models more interpretable without sacrificing predictive performance. The motivation for XAI is deeply rooted in the necessity to ensure that AI-driven decisions can be scrutinized, justified, and audited, especially in high-stakes applications. For instance, in healthcare settings, the inability of an AI system to explain its recommendations may limit its adoption due to concerns over patient safety and regulatory compliance [1]. Similarly, in finance, regulatory frameworks increasingly require that automated decisions be transparent and auditable to prevent unfair or biased outcomes [3][2]. A central focus within XAI research is the development of model-agnostic techniques. These approaches treat the underlying machine learning model as a black box and generate explanations based solely on input-output behavior. Model-agnostic methods offer flexibility since they can be applied across a wide range of algorithms without requiring access to internal model parameters or architecture details [4]. This universality is particularly valuable given the rapid evolution of machine learning models and the need for explanation tools that remain relevant as new architectures emerge. Among model-agnostic techniques, local and global explanation strategies have gained prominence. Local explanations aim to clarify individual predictions by identifying which features contributed most significantly to a specific output. Methods such as LIME (Local Interpretable Model-agnostic Explanations) approximate the model's decision boundary in the vicinity of a particular instance using simpler surrogate models, thereby offering interpretable insights into why a certain prediction was made [5][4][6]. In contrast, global explanations seek to provide an overarching view of how input features influence predictions across the entire dataset. Techniques like SHAP (SHapley Additive exPlanations) quantify feature contributions both locally and globally by leveraging concepts from cooperative game theory [4][6]. Feature importance attribution remains a cornerstone of many XAI methods. By quantifying how much each input variable contributes to a model's prediction, these techniques help users understand which factors drive decisions. However, while technical advances have produced sophisticated mathematical tools for attributing importance or visualizing saliency maps, such as RISE or XRAI for image data, their effectiveness in real-world settings is not always guaranteed [7]. Qualitative studies indicate that technical explanations do not necessarily translate into improved user understanding or trust among non-expert stakeholders [8]. This highlights an important limitation: explanations must be tailored not only for technical accuracy but also for comprehensibility by diverse audiences. Transparency is closely linked with explainability; providing clear rationales for model outputs enhances users' ability to assess reliability and fosters greater acceptance of AI systems. Explanations can take various forms, including textual descriptions, visualizations, or feature-based summaries, and each modality offers distinct advantages depending on user expertise and context. For example, survey-based research in autonomous vehicles demonstrates that users prefer explanations combining confidence scores with contextual information about prior situations rather than isolated metrics devoid of background reasoning [3]. Such findings underscore the importance of designing explanation interfaces that align with human cognitive preferences. Despite these advances, there are inherent trade-offs between accuracy and interpretability. Highly accurate models tend to be more complex and less transparent by nature. Conversely, simpler models may offer greater interpretability at the expense of predictive power [5]. The challenge lies in balancing these competing objectives so that AI systems remain both effective and trustworthy. Furthermore, while explainability offers clear benefits, such as increased trustworthiness and accountability, it also introduces potential risks if not carefully managed. Over-reliance on imperfect explanations or misinterpretation of model rationales can lead to inappropriate trust calibration or even harmful outcomes in sensitive domains [9]. Therefore, it is crucial that XAI methods are rigorously evaluated not only for their technical soundness but also for their impact on end-user decision-making processes. The ongoing evolution of XAI reflects a broader recognition that transparency is foundational for responsible AI deployment across sectors where human lives and societal values are at stake. As regulatory pressures mount and public scrutiny intensifies, developing robust explanation techniques will remain central to ensuring that machine learning systems are both effective and aligned with ethical standards.