[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118694-en":3,"doc-seo-118694-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":11},118694,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Interpretability and Explainability in Machine Learning Systems - Survey","Interpretability and explainability are critical for machine learning (ML) systems deployed in high-stakes settings where transparency and trust matter. This survey reviews key definitions and organizes methods into inherently interpretable models, model-specific explanation techniques, and model-agnostic post-hoc approaches. It examines trade-offs between predictive performance and transparency, discusses evaluation metrics such as fidelity and interpretability, and highlights limitations and emerging directions to improve user trust and regulatory compliance.","Interpretability and Explainability in Machine  \nLearning Systems  \nProf. Dr. Gurpreet Singh  \nVice Principal, JBTT  \n[gurpreetkhator@gmail.com](gurpreetkhator@gmail.com)  \nAbstract—Interpretability and explainability are critical aspects of machine learning (ML) systems, especially when deployed in high-stakes domains. This survey reviews key definitions, techniques, and challenges associated with interpretability and explainability in ML. We categorize approaches into inherently interpretable models, model-specific explanations, and model-agnostic post-hoc methods. The paper discusses trade-offs between model performance and transparency, evaluation metrics, and emerging directions to enhance user trust and regulatory compliance.  \nIndex Terms—Machine Learning, Interpretability, Explainability, Explainable AI, Model Transparency, Post-hoc Explanations, Trustworthiness  \nI. Introduction  \nMachine learning systems have demonstrated remarkable performance across various domains, including healthcare, finance, and autonomous driving. However, the black-box nature of many state-of-the-art models raises concerns regarding their transparency and trustworthiness. Interpretability refers to the extent to which a human can understand the internal mechanics of a system, whereas explainability focuses on how a model’s outputs can be explained in humanunderstandable terms [1], [2] . This survey aims to systematically review the landscape of interpretability and explainability methods, highlighting their applications, limitations, and evaluation criteria.  \nII. Definitions and Importance  \nInterpretability and explainability are sometimes used interchangeably but differ subtly. According to [3], interpretability is the degree to which a human can consistently predict the model’s output, whereas explainability is the ability to provide understandable reasons for specific decisions. Both aspects are vital in domains with ethical, legal, or safety implications [4] .  \nIII. Categories of Interpretability and Explainability  \nInterpretability and explainability methods in machine learning (ML) can be broadly classified based on their approach, model dependency, and scope of application. This section outlines the major categories commonly adopted in the literature.  \nA. Inherently Interpretable Models  \nInherently interpretable models are designed such that their decision-making process is transparent and understandable without the need for additional explanation tools. Examples include:  \n• Linear Models: Linear regression and logistic regression models provide direct insight into feature contributions through model coefficients.  \n• Decision Trees: The tree structure enables tracing decisions along clear paths from input features to predictions.  \n• Rule-Based Models: Expert systems and models based on logical rules offer explanations in human-readable formats.  \nWhile these models offer transparency, they may lack the expressive power required for complex tasks, often resulting in lower predictive accuracy compared to black-box models [1], [2] .  \nB. Model-Specific Explanation Methods  \nModel-specific methods generate explanations tailored to particular classes of models by leveraging their internal structure. These methods include:  \n• Saliency Maps and Gradient-Based Approaches: Commonly used with neural networks, these highlight input features most influential in generating outputs, such as GradCAM and Integrated Gradients [3], [4] .  \n• Attention Mechanisms: Attention weights in transformer-based models reveal the relative importance of input elements during prediction [5] .  \n• Layer-Wise Relevance Propagation: This technique decomposes a prediction backward through the network layers to attribute relevance scores to inputs [6] .  \nThese methods typically produce more faithful explanations but are limited to specific model architectures.  \nC. Model-Agnostic Post-Hoc Methods  \nModel-agnostic post-hoc techniques provide explanations for a","cbCaigBZxDLQDXZ9","https://ap.wps.com/l/cbCaigBZxDLQDXZ9","pdf",364352,1,3,"English","en",105,"# Introduction\n# Definitions and Importance\n# Categories of Interpretability and Explainability\n## Inherently Interpretable Models\n## Model-Specific Explanation Methods\n## Model-Agnostic Post-Hoc Methods\n## Hybrid Approaches\n# Evaluation of Interpretability and Explainability\n## Evaluation Criteria","[{\"question\":\"What is the difference between interpretability and explainability in ML?\",\"answer\":\"Interpretability measures how consistently a human can predict the model’s output, while explainability focuses on providing understandable reasons for specific decisions.\"},{\"question\":\"What are the main categories of interpretability and explainability methods?\",\"answer\":\"The survey groups methods into inherently interpretable models, model-specific explanation methods, and model-agnostic post-hoc methods, with additional attention to hybrid approaches.\"},{\"question\":\"How are interpretability and explainability quality commonly evaluated?\",\"answer\":\"Evaluation commonly considers fidelity (descriptive accuracy) and interpretability (simplicity), assessing how accurately explanations reflect model behavior and how easily humans can understand them.\"}]","Interpretability and Explainability in Machine Learning Systems - Survey | PDF",1785684923,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"interpretability-and-explainability-in-machine-learning-systems-survey","",{"@graph":35,"@context":84},[36,52,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":21},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/interpretability-and-explainability-in-machine-learning-systems-survey/118694/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-04","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the difference between interpretability and explainability in ML?","Question",{"text":74,"@type":75},"Interpretability measures how consistently a human can predict the model’s output, while explainability focuses on providing understandable reasons for specific decisions.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What are the main categories of interpretability and explainability methods?",{"text":79,"@type":75},"The survey groups methods into inherently interpretable models, model-specific explanation methods, and model-agnostic post-hoc methods, with additional attention to hybrid approaches.",{"name":81,"@type":72,"acceptedAnswer":82},"How are interpretability and explainability quality commonly evaluated?",{"text":83,"@type":75},"Evaluation commonly considers fidelity (descriptive accuracy) and interpretability (simplicity), assessing how accurately explanations reflect model behavior and how easily humans can understand them.","https://schema.org",{"og:url":50,"og:type":86,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":88,"canonical":50},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]