resmilitaris最新文献_第4页

IT Infrastructure Management: Optimizing Performance, Scalability, and Reliability IT 基础设施管理：优化性能、可扩展性和可靠性

resmilitaris Pub Date : 2024-03-01 DOI: 10.48047/resmil.v9i1.30

Jugendra Singh, Nisha Sharma, S. Saini

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GENERATIVE ADVERSARIAL NETWORKS APPLICATIONS 生成式对抗网络的应用

resmilitaris Pub Date : 2024-03-01 DOI: 10.48047/resmil.v10i1.13

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IoT Device Management and Security 物联网设备管理与安全

resmilitaris Pub Date : 2024-03-01 DOI: 10.48047/resmil.v10i1.17

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IoT Security in Smart Homes 智能家居中的物联网安全

resmilitaris Pub Date : 2024-03-01 DOI: 10.48047/resmil.v9i1.31

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Food waste reduction technologies 减少食物垃圾技术

resmilitaris Pub Date : 2024-03-01 DOI: 10.48047/resmil.v9i1.25

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Responsive Web Design with HTML and CSS 使用 HTML 和 CSS 进行响应式网页设计

resmilitaris Pub Date : 2024-03-01 DOI: 10.48047/resmil.v9i1.22

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Distributed Computing with Dask and Apache Spark: A Comparative Study 使用 Dask 和 Apache Spark 的分布式计算：比较研究

resmilitaris Pub Date : 2024-03-01 DOI: 10.48047/resmil.v9i1.21

Ankita Jain, Devendra Singh Sendar, Sarita Mahajan

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Explainable AI (XAI): Bridging the Gap between Machine Learning and Human Understanding 可解释的人工智能（XAI）：缩小机器学习与人类理解之间的差距

resmilitaris Pub Date : 2024-03-01 DOI: 10.48047/resmil.v10i1.19

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Introduction to Data Visualization Techniques: A Comparative Analysis 数据可视化技术简介：比较分析

resmilitaris Pub Date : 2024-03-01 DOI: 10.48047/resmil.v9i1.32

Yogesh Bhomia

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Cracking the Code: Enhancing Trust in AI through Explainable Models 破解密码：通过可解释模型增强对人工智能的信任

resmilitaris Pub Date : 2024-03-01 DOI: 10.48047/resmil.v10i1.20

Vipin Gupta, Shailendra Shukla, Kumari Nikita

{"title":"Cracking the Code: Enhancing Trust in AI through Explainable Models","authors":"Vipin Gupta, Shailendra Shukla, Kumari Nikita","doi":"10.48047/resmil.v10i1.20","DOIUrl":"https://doi.org/10.48047/resmil.v10i1.20","url":null,"abstract":"In this paper, we explore the critical challenges of building trust in artificial intelligence (AI) systems, particularly those characterized by black box models. The proliferation of complex and opaque AI models has raised concerns about a lack of interpretability, hindering users’ understanding and confidence in these systems Significant problem solved in this review addresses the importance of increasing the reliability of AI through semantic AI (XAI) approaches . clarify the complexity of the model To address this issue, our approach is a comprehensive review of the existing literature on XAI, black-box models, and their implications for reliability We thoroughly analyze various XAI methods, such as local interpretive model-agnostic explanations (LIME), SHapley explanatory agnostic explanations (SHAP). and reflection methods, in addition to clarifying their efforts aimed at making AI models transparent, we examine real-world case studies in which the use of XAI has enhanced trustworthiness of AI systems have improved in various sectors. The main findings of our study highlight the important role of XAI in reducing the uncertainty associated with black-box models. We highlight examples where the adoption of interpretable approaches not only increased the interpretability of AI systems but also enhanced user confidence. By providing transparent insights into decision-making processes, XAI is proving to help remove complex models and establish a foundation of trust between users and AI systems The implications of our research apply to a range of industries that rely on AI, including healthcare, finance and autonomous systems. While opening up the benefits of XAI for building trust, we recommend its inclusion in AI development practices and highlight possible future developments in this area. However, our study acknowledges the existing","PeriodicalId":517991,"journal":{"name":"resmilitaris","volume":"91 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140405791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0