With the rapid expansion in the use of Artificial Intelligence (AI) technologies across various sensitive sectors such as healthcare, finance, justice, and defense, a pressing need has emerged to understand how these systems make decisions. This has driven researchers to develop a new branch of AI known as Explainable Artificial Intelligence (XAI), which aims to make intelligent models more transparent, understandable, and auditable by humans.<br />What is Explainable Artificial Intelligence (XAI)?<br />Explainable AI is a subfield of Artificial Intelligence that focuses on developing models and techniques whose decisions and outputs can be explained to human users in a clear and understandable way, without compromising performance or accuracy. Unlike traditional "black-box" models that deliver results without justification, XAI models aim to answer key questions such as:<br />• Why did the model make this decision?<br />• What factors influenced the result?<br />• Can this result be trusted?<br />• How sensitive is the decision to changes in the input?<br />The Importance of Explainability<br />1. Transparency and Trust<br />Interpretability provides clarity for the end user and enhances system credibility.<br />2. Support for Human Decision-Making<br />Professionals (e.g., doctors or judges) can rely more effectively on AI when they understand the basis of its decisions.<br />3. Regulatory and Ethical Compliance<br />Some regulations (such as GDPR in Europe) grant individuals the right to understand the logic behind automated decisions.<br />4. Model Improvement<br />Explanation helps in identifying errors and biases, contributing to the overall improvement of model performance and reliability.<br />XAI Methods: Interpretation and Analysis Techniques<br />Explainability techniques are generally categorized into two main types:<br />1. Post-hoc Explainability<br />These methods are applied after the model is trained. Well-known techniques include:<br />• LIME (Local Interpretable Model-Agnostic Explanations): Explains the decisions of a complex model using simplified local approximations.<br />• SHAP (SHapley Additive exPlanations): Quantifies each feature’s contribution to the final decision.<br />• Grad-CAM: Used to interpret decisions made by neural networks in computer vision tasks.<br />2. Inherently Interpretable Models<br />These models are transparent by design, such as:<br />• Decision Trees<br />• Linear Models<br />• Rule-Based Systems<br />Challenges in Explainable AI<br />• Trade-off Between Accuracy and Interpretability<br />Interpretable models are often simpler and may exhibit lower accuracy compared to complex deep learning models.<br />• Multiplicity of Explanations<br />Different techniques may produce varying interpretations for the same decision.<br />• User Comprehension<br />Explanations may be clear to experts but difficult to understand for general users.<br />Applications of XAI<br />1. Healthcare<br />Explaining why an AI system predicts the presence of a specific disease based on medical imaging.<br />2. Financial Sector<br />Clarifying the reasons for loan denial through analysis of customer data.<br />3. Legal Systems<br />Supporting court decisions using transparent, unbiased reasoning from AI systems.<br />4. Cybersecurity<br />Justifying why certain behavior is flagged as a threat or anomaly.<br />Al-Mustaqbal University – The First University in Iraq.<br />