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市場調查報告書
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1916678

可解釋人工智慧 (XAI) 市場，全球預測至 2032 年：按組件、模型類型、部署模式、技術、最終用戶和地區分類

Explainable AI (XAI) Market Forecasts to 2032 - Global Analysis By Component (Software and Services), Model Type, Deployment Model, Technology, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的一項研究，全球可解釋人工智慧 (XAI) 市場預計到 2025 年價值 91.9 億美元，到 2032 年達到 292.8 億美元，在預測期內以 18% 的複合年成長率成長。

可解釋人工智慧 (XAI) 指的是一系列旨在使人工智慧模型決策、預測和行為對人類透明、可解釋和可理解的技術和系統。與「黑箱」人工智慧系統不同，XAI 可以清晰地視覺化輸入資料如何影響結果，使用戶能夠追蹤推理過程並檢驗結果。 XAI 使相關人員能夠評估人工智慧模型的公平性、可靠性和偏差，從而有助於建立信任、確保課責並支持監管合規。這在醫療保健、金融、國防和自主系統等高影響力領域尤其重要，因為在這些領域，理解人工智慧驅動的決策至關重要。

監管機構對人工智慧透明度的要求日益提高

政策制定者正在強制要求人工智慧系統具備可解釋性，以確保課責和公平性。企業也越來越需要可解釋人工智慧（XAI）框架來檢驗金融、醫療和政府應用中的決策。供應商正在將透明度模組整合到其人工智慧平台中，以加強合規性和信任度。對可解釋模型日益成長的需求正在推動受監管行業的廣泛應用。對透明度的追求正將可解釋性從小眾功能轉變為人工智慧部署的主流要求。

實現可解釋性的複雜性很高

開發既能保持準確性又不犧牲性能的可解釋模型在技術上極具挑戰性。由於資源限制，企業難以將可解釋性整合到現有的人工智慧工作流程中。與擁有先進研發能力的大型企業相比，中小企業面臨的障礙更大。供應商正在探索混合方法，以平衡透明度和效率。這種複雜性減緩了可解釋性的應用，使其成為人工智慧創新領域一個充滿挑戰的前沿領域。

在受監管產業中擴大應用

金融服務業對透明人工智慧的需求日益成長，以支援信用評分、詐欺檢測和合規性審核。醫療服務提供者正在將可解釋模型融入診斷系統，以增強患者信任並獲得監管部門的核准。各國政府正投資可解釋人工智慧框架，以改善公共服務決策。供應商正在客製化解決方案，以滿足特定產業的合規標準。受監管產業不僅在推動可解釋人工智慧（XAI）的應用，而且還將其定位為建立合乎倫理且值得信賴的人工智慧生態系統的關鍵基礎。

缺乏標準化的可解釋性框架

由於缺乏統一的指導方針，企業在選擇合適的調查方法時面臨不確定性。監管機構尚未制定統一的透明度標準，這使得合規變得更加複雜。供應商必須根據不同的地區和特定產業要求調整其解決方案。這種缺乏標準化的做法增加了供應商的成本，並減緩了擴充性。如果沒有明確的框架，可解釋性將持續存在不一致的風險，從而有可能損害全球市場對人工智慧系統的信任。

新冠疫情的影響：

新冠疫情加速了對可解釋人工智慧的需求，因為企業對自動化系統的依賴性日益增強。同時，研發中斷和計劃延期減緩了透明度工具的普及。然而，醫療保健和公共領域對可信賴人工智慧日益成長的需求推動了其應用。各組織越來越依賴可解釋模型來檢驗危機期間所做的決策。供應商將可解釋性功能整合到其人工智慧平台中，以增強系統的韌性和合規性。疫情也凸顯了透明度在保障人工智慧主導的決策在不確定環境下安全的重要性。

在預測期內，軟體領域將佔據最大的市場佔有率。

在預測期內，軟體領域預計將佔據最大的市場佔有率，這主要得益於人工智慧平台對整合透明度模組的需求。軟體解決方案使企業能夠將可解釋性直接融入機器學習工作流程。供應商正在投資開發先進的視覺化和模型解釋工具，以提高可用性。對可擴展和模組化解決方案日益成長的需求正在推動該領域的應用。企業發現，軟體驅動的可解釋性對於合規性和建立信任至關重要。軟體的主導地位反映了其作為基礎層的作用，該基礎層能夠實現各種人工智慧應用的透明度。

在預測期內，深度學習可解釋性細分市場將呈現最高的複合年成長率。

預計在預測期內，深度學習可解釋性領域將實現最高成長率，這主要得益於對複雜神經網路透明度日益成長的需求。深度學習模型通常以黑箱形式運行，這給課責帶來了挑戰。供應商正在將SHAP、LIME和基於注意力機制的方法等可解釋性技術整合到其框架中。企業正在採用這些解決方案來增強對自主系統和高階分析的信任。對深度學習應用的投資不斷增加，也推動了該領域的需求。深度學習可解釋性的成長凸顯了在下一代人工智慧中兼顧效能和透明度的重要性。

佔比最大的地區：

由於北美地區擁有成熟的人工智慧基礎設施和對透明度的高度監管，預計該地區將在預測期內佔據最大的市場佔有率。美國和加拿大的公司在投資可解釋框架以滿足合規標準方面處於主導。主要技術供應商的存在進一步鞏固了該地區的領先地位。金融、醫療保健和政府部門對符合倫理的人工智慧日益成長的需求正在推動其應用。供應商正在整合先進的可解釋性模組，以在競爭激烈的市場中脫穎而出。

複合年成長率最高的地區：

亞太地區預計將在預測期內實現最高的複合年成長率，這主要得益於快速的都市化、人工智慧應用的日益普及以及政府主導的數位化舉措。中國、印度和東南亞等國家正大力投資解釋人工智慧，以支持其金融科技、醫療保健和智慧城市生態系統的發展。該地區的企業正在採用可解釋人工智慧框架來加強合規性並滿足消費者信任需求。本地Start-Ups正在為各行各業部署客製化、具成本效益的解決方案。政府推行的旨在促進合乎倫理的人工智慧和透明度的計畫正在加速人工智慧的普及應用。

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目錄

第1章執行摘要

第2章 前言

  • 概括
  • 相關利益者
  • 調查範圍
  • 調查方法
  • 研究材料

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球可解釋人工智慧 (XAI) 市場（按組件分類）

  • 軟體
    • XAI框架庫
    • 模型解釋和說明工具
    • 可視化和彙報解決方案
    • 人工智慧管治、審核和合規平台
  • 服務
    • 諮詢和顧問服務
    • 整合和實施服務
    • 託管服務和平台支持

6. 全球可解釋人工智慧 (XAI) 市場按模型類型分類

  • 監督學習中的可解釋性
  • 無監督學習中的可解釋性
  • 強化學習中的可解釋性
  • 整合模型和混合模式

7. 全球可解釋人工智慧 (XAI) 市場按部署模式分類

  • 本地部署
  • 基於雲端的

8. 全球可解釋人工智慧 (XAI) 市場（按技術分類）

  • 機器學習可解釋性
  • 深度學習可解釋性
  • 自然語言處理可解釋性
  • 電腦視覺可解釋性
  • 其他

9. 全球可解釋人工智慧 (XAI) 市場（按最終用戶分類）

  • 醫學與生命科​​學
  • 零售/電子商務
  • 資訊科技/通訊
  • 製造和工業自動化
  • 能源與公用事業
  • 政府/公共部門
  • 其他

10. 全球可解釋人工智慧 (XAI) 市場（按地區分類）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 亞太其他地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美國家
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第11章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 併購
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第12章：企業概況

  • IBM Corporation
  • Microsoft Corporation
  • Oracle Corporation
  • SAP SE
  • SAS Institute Inc.
  • Google LLC
  • Amazon Web Services, Inc.
  • Fiddler AI, Inc.
  • DarwinAI Corp.
  • Kyndi, Inc.
  • H2O.ai, Inc.
  • DataRobot, Inc.
  • Seldon Technologies Ltd.
  • Peltarion AB
  • Zest AI
Product Code: SMRC33251

According to Stratistics MRC, the Global Explainable AI (XAI) Market is accounted for $9.19 billion in 2025 and is expected to reach $29.28 billion by 2032 growing at a CAGR of 18% during the forecast period. Explainable Artificial Intelligence (XAI) refers to a set of methods and systems designed to make the decisions, predictions, and behaviors of artificial intelligence models transparent, interpretable, and understandable to humans. Unlike "black-box" AI systems, XAI provides clear insights into how input data influences outputs, enabling users to trace reasoning processes and validate results. XAI helps build trust, ensures accountability, and supports regulatory compliance by allowing stakeholders to assess fairness, reliability, and bias in AI models. It is especially critical in high-impact domains such as healthcare, finance, defense, and autonomous systems, where understanding AI-driven decisions is essential.

Market Dynamics:

Driver:

Growing regulatory demand for AI transparency

Policymakers are mandating explainability in AI systems to ensure accountability and fairness. Enterprises increasingly require XAI frameworks to validate decisions in finance, healthcare, and government applications. Vendors are embedding transparency modules into AI platforms to strengthen compliance and trust. Rising demand for interpretable models is reinforcing adoption across regulated industries. The push for transparency is transforming explainability from a niche capability into a mainstream requirement for AI deployment.

Restraint:

High complexity of explainability implementation

Developing interpretable models that maintain accuracy without sacrificing performance is technically challenging. Enterprises struggle to integrate explainability into existing AI workflows due to resource constraints. Smaller firms face higher barriers compared to large incumbents with advanced R&D capabilities. Vendors are experimenting with hybrid approaches to balance transparency and efficiency. This complexity is slowing widespread adoption, making explainability a demanding frontier in AI innovation.

Opportunity:

Expanding use in regulated industries

Financial services increasingly require transparent AI to support credit scoring, fraud detection, and compliance audits. Healthcare providers are embedding explainable models into diagnostic systems to strengthen patient trust and regulatory approval. Governments are investing in interpretable AI frameworks to improve decision-making in public services. Vendors are tailoring solutions to meet industry-specific compliance standards. Regulated industries are not only driving adoption but positioning XAI as a critical enabler of ethical and trustworthy AI ecosystems.

Threat:

Lack of standardized explainability frameworks

Enterprises face uncertainty in selecting appropriate methodologies due to fragmented guidelines. Regulators have yet to establish unified benchmarks for transparency which complicates compliance. Vendors must adapt solutions to diverse regional and industry-specific requirements. This lack of standardization increases costs and slows scalability for providers. Without clear frameworks, explainability risks remaining inconsistent, undermining trust in AI systems across global markets.

Covid-19 Impact:

The Covid-19 pandemic accelerated demand for explainable AI as enterprises faced surging reliance on automated systems. On one hand, disruptions in R&D and delayed projects slowed deployment of transparency tools. On the other hand, rising demand for trustworthy AI in healthcare and public safety boosted adoption. Organizations increasingly relied on interpretable models to validate decisions during crisis conditions. Vendors embedded explainability features into AI platforms to strengthen resilience and compliance. The pandemic highlighted the importance of transparency as a safeguard for AI-driven decision-making in uncertain environments.

The software segment is expected to be the largest during the forecast period

The software segment is expected to account for the largest market share during the forecast period, driven by demand for integrated transparency modules in AI platforms. Software solutions enable enterprises to embed explainability directly into machine learning workflows. Vendors are investing in advanced visualization and model interpretation tools to improve usability. Rising demand for scalable and modular solutions is reinforcing adoption in this segment. Enterprises view software-driven explainability as critical for compliance and trust-building. The dominance of software reflects its role as the foundation layer enabling transparency across diverse AI applications.

The deep learning explainability segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the deep learning explainability segment is predicted to witness the highest growth rate, supported by rising demand for transparency in complex neural networks. Deep learning models often operate as black boxes, creating challenges for accountability. Vendors are embedding interpretability techniques such as SHAP, LIME, and attention-based methods into frameworks. Enterprises are adopting these solutions to strengthen trust in autonomous systems and advanced analytics. Rising investment in deep learning applications is reinforcing demand in this segment. The growth of deep learning explainability highlights its role in bridging performance with transparency in next-generation AI.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share by mature AI infrastructure and strong regulatory emphasis on transparency. Enterprises in the United States and Canada are leading investments in explainable frameworks to meet compliance standards. The presence of major technology vendors further strengthens regional dominance. Rising demand for ethical AI in finance, healthcare, and government is reinforcing adoption. Vendors are embedding advanced explainability modules to differentiate offerings in competitive markets.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapid urbanization, expanding AI adoption, and government-led digital initiatives. Countries such as China, India, and Southeast Asia are investing heavily in explainable AI to support fintech, healthcare, and smart city ecosystems. Enterprises in the region are adopting XAI frameworks to strengthen compliance and meet consumer trust requirements. Local startups are deploying cost-effective solutions tailored to diverse industries. Government programs promoting ethical AI and transparency are accelerating adoption.

Key players in the market

Some of the key players in Explainable AI (XAI) Market include IBM Corporation, Microsoft Corporation, Oracle Corporation, SAP SE, SAS Institute Inc., Google LLC, Amazon Web Services, Inc., Fiddler AI, Inc., DarwinAI Corp., Kyndi, Inc., H2O.ai, Inc., DataRobot, Inc., Seldon Technologies Ltd., Peltarion AB and Zest AI.

Key Developments:

In October 2023, SAP and Microsoft expanded their partnership to integrate SAP's responsible AI and data ethics capabilities with Microsoft's Azure OpenAI Service. This collaboration, announced at SAP TechEd, specifically aimed to provide greater transparency and control for generative AI models used in enterprise processes, embedding XAI principles into joint solutions.

In May 2022, Microsoft Research partnered with this MIT center to fund and conduct fundamental research on intelligence and cognition, which includes interdisciplinary work on making AI decision-making processes more transparent and aligned with human reasoning.

Components Covered:

  • Software
  • Services

Model Types Covered:

  • Supervised Learning Explainability
  • Unsupervised Learning Explainability
  • Reinforcement Learning Explainability
  • Ensemble & Hybrid Models

Deployment Models Covered:

  • On-Premise
  • Cloud-Based

Technologies Covered:

  • Machine Learning Explainability
  • Deep Learning Explainability
  • Natural Language Processing Explainability
  • Computer Vision Explainability
  • Other Technologies

End Users Covered:

  • Healthcare and Life Sciences
  • Retail and E-Commerce
  • IT and Telecommunications
  • Manufacturing and Industrial Automation
  • Energy and Utilities
  • Government and Public Sector
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Explainable AI (XAI) Market, By Component

  • 5.1 Introduction
  • 5.2 Software
    • 5.2.1 XAI Frameworks and Libraries
    • 5.2.2 Model Interpretation and Explanation Tools
    • 5.2.3 Visualization and Reporting Solutions
    • 5.2.4 AI Governance, Audit, and Compliance Platforms
  • 5.3 Services
    • 5.3.1 Consulting and Advisory Services
    • 5.3.2 Integration and Implementation Services
    • 5.3.3 Managed Services and Platform Support

6 Global Explainable AI (XAI) Market, By Model Type

  • 6.1 Introduction
  • 6.2 Supervised Learning Explainability
  • 6.3 Unsupervised Learning Explainability
  • 6.4 Reinforcement Learning Explainability
  • 6.5 Ensemble & Hybrid Models

7 Global Explainable AI (XAI) Market, By Deployment Model

  • 7.1 Introduction
  • 7.2 On-Premise
  • 7.3 Cloud-Based

8 Global Explainable AI (XAI) Market, By Technology

  • 8.1 Introduction
  • 8.2 Machine Learning Explainability
  • 8.3 Deep Learning Explainability
  • 8.4 Natural Language Processing Explainability
  • 8.5 Computer Vision Explainability
  • 8.8 Other Technologies

9 Global Explainable AI (XAI) Market, By End User

  • 9.1 Introduction
  • 9.2 Healthcare and Life Sciences
  • 9.3 Retail and E-Commerce
  • 9.4 IT and Telecommunications
  • 9.5 Manufacturing and Industrial Automation
  • 9.6 Energy and Utilities
  • 9.7 Government and Public Sector
  • 9.8 Other End Users

10 Global Explainable AI (XAI) Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 IBM Corporation
  • 12.2 Microsoft Corporation
  • 12.3 Oracle Corporation
  • 12.4 SAP SE
  • 12.5 SAS Institute Inc.
  • 12.6 Google LLC
  • 12.7 Amazon Web Services, Inc.
  • 12.8 Fiddler AI, Inc.
  • 12.9 DarwinAI Corp.
  • 12.10 Kyndi, Inc.
  • 12.11 H2O.ai, Inc.
  • 12.12 DataRobot, Inc.
  • 12.13 Seldon Technologies Ltd.
  • 12.14 Peltarion AB
  • 12.15 Zest AI

List of Tables

  • Table 1 Global Explainable AI (XAI) Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Explainable AI (XAI) Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global Explainable AI (XAI) Market Outlook, By Software (2024-2032) ($MN)
  • Table 4 Global Explainable AI (XAI) Market Outlook, By XAI Frameworks and Libraries (2024-2032) ($MN)
  • Table 5 Global Explainable AI (XAI) Market Outlook, By Model Interpretation and Explanation Tools (2024-2032) ($MN)
  • Table 6 Global Explainable AI (XAI) Market Outlook, By Visualization and Reporting Solutions (2024-2032) ($MN)
  • Table 7 Global Explainable AI (XAI) Market Outlook, By AI Governance, Audit, and Compliance Platforms (2024-2032) ($MN)
  • Table 8 Global Explainable AI (XAI) Market Outlook, By Services (2024-2032) ($MN)
  • Table 9 Global Explainable AI (XAI) Market Outlook, By Consulting and Advisory Services (2024-2032) ($MN)
  • Table 10 Global Explainable AI (XAI) Market Outlook, By Integration and Implementation Services (2024-2032) ($MN)
  • Table 11 Global Explainable AI (XAI) Market Outlook, By Managed Services and Platform Support (2024-2032) ($MN)
  • Table 12 Global Explainable AI (XAI) Market Outlook, By Model Type (2024-2032) ($MN)
  • Table 13 Global Explainable AI (XAI) Market Outlook, By Supervised Learning Explainability (2024-2032) ($MN)
  • Table 14 Global Explainable AI (XAI) Market Outlook, By Unsupervised Learning Explainability (2024-2032) ($MN)
  • Table 15 Global Explainable AI (XAI) Market Outlook, By Reinforcement Learning Explainability (2024-2032) ($MN)
  • Table 16 Global Explainable AI (XAI) Market Outlook, By Ensemble & Hybrid Models (2024-2032) ($MN)
  • Table 17 Global Explainable AI (XAI) Market Outlook, By Deployment Model (2024-2032) ($MN)
  • Table 18 Global Explainable AI (XAI) Market Outlook, By On-Premise (2024-2032) ($MN)
  • Table 19 Global Explainable AI (XAI) Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 20 Global Explainable AI (XAI) Market Outlook, By Technology (2024-2032) ($MN)
  • Table 21 Global Explainable AI (XAI) Market Outlook, By Machine Learning Explainability (2024-2032) ($MN)
  • Table 22 Global Explainable AI (XAI) Market Outlook, By Deep Learning Explainability (2024-2032) ($MN)
  • Table 23 Global Explainable AI (XAI) Market Outlook, By Natural Language Processing Explainability (2024-2032) ($MN)
  • Table 24 Global Explainable AI (XAI) Market Outlook, By Computer Vision Explainability (2024-2032) ($MN)
  • Table 25 Global Explainable AI (XAI) Market Outlook, By Other Technologies (2024-2032) ($MN)
  • Table 26 Global Explainable AI (XAI) Market Outlook, By End User (2024-2032) ($MN)
  • Table 27 Global Explainable AI (XAI) Market Outlook, By Healthcare and Life Sciences (2024-2032) ($MN)
  • Table 28 Global Explainable AI (XAI) Market Outlook, By Retail and E-Commerce (2024-2032) ($MN)
  • Table 29 Global Explainable AI (XAI) Market Outlook, By IT and Telecommunications (2024-2032) ($MN)
  • Table 30 Global Explainable AI (XAI) Market Outlook, By Manufacturing and Industrial Automation (2024-2032) ($MN)
  • Table 31 Global Explainable AI (XAI) Market Outlook, By Energy and Utilities (2024-2032) ($MN)
  • Table 32 Global Explainable AI (XAI) Market Outlook, By Government and Public Sector (2024-2032) ($MN)
  • Table 33 Global Explainable AI (XAI) Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.