可解释的人工智慧市场 - 全球产业规模、份额、趋势、机会和预测，按组件、部署、按应用、最终用途、地区、竞争细分，2018-2028

2022 年全球可解释人工智慧市场价值为 54 亿美元，预计在预测期内将强劲成长，到 2028 年复合CAGR为22.4%。随着组织越来越多地采用人工智慧，全球可解释人工智慧(XAI) 市场正在经历显着成长跨各产业的解决方案。 XAI是指人工智慧系统为其决策和行动提供可理解和可解释的解释的能力，解决传统人工智慧的「黑盒子」挑战。在对透明度、问责制和道德人工智慧部署日益增长的需求的推动下，市场即将扩张。 XAI 在金融、医疗保健和自动驾驶汽车等领域至关重要，在这些领域，理解人工智慧产生的决策的能力对于监管合规性和用户信任至关重要。此外，人工智慧相关法规和指南的兴起进一步推动了对 XAI 解决方案的需求。该市场的特点是机器学习技术、演算法和模型架构的创新，这些创新增强了人工智慧系统的可解释性。随着企业优先考虑负责任的人工智慧实践，可解释的人工智慧市场将继续其成长轨迹，提供的解决方案不仅能提供人工智慧驱动的见解，还能确保透明度和以人为本的人工智慧决策过程。

主要市场驱动因素

决策的透明度

市场概况
预测期	2024-2028
2022 年市场规模	54亿美元
2028 年市场规模	183.2亿美元
2023-2028 年CAGR	22.4%
成长最快的细分市场	云
最大的市场	北美洲

由于人工智慧 (AI) 系统对透明度和可解释性的需求不断增长，全球可解释人工智慧 (XAI) 市场正在显着成长。 XAI 在医疗保健、金融和自动驾驶汽车等各个领域发挥着至关重要的作用，在这些领域，理解人工智慧系统所做的决策对于监管合规性和用户信任至关重要。随着人工智慧的日益普及，需要解决人工智慧模型和演算法的复杂性，这使得 XAI 解决方案变得越来越不可或缺。市场的繁荣得益于机器学习技术和演算法的不断创新，这些技术和演算法增强了人工智慧系统的可解释性，确保组织能够利用人工智慧的力量，同时坚持问责制和道德人工智慧实践。

监理合规性

由于与人工智慧相关的法规和指南数量不断增加，可解释人工智慧（XAI）的全球市场正在经历显着增长。政府和产业监管机构非常重视人工智慧道德实践，这迫使组织采用 XAI 解决方案来满足合规性要求。随着监管框架的不断发展，XAI 在帮助组织确保其人工智慧系统遵守法律和道德标准方面发挥着至关重要的作用。在监管要求的推动下，对 XAI 的需求不断增长，在资料隐私、公平和问责至关重要的行业中尤为突出。全球范围内人工智慧相关法规和指南的激增，为XAI市场的蓬勃发展创造了有利的环境。政府和监管机构正在认识到与缺乏透明度和可解释性的人工智慧系统相关的潜在风险。因此，他们正在采取措施，确保负责任地开发和部署人工智慧技术。这些法规通常要求组织对其人工智慧系统所做的决策提供解释，特别是在医疗保健、金融和刑事司法等关键领域。透过采用 XAI 解决方案，组织可以满足这些监管要求并展示其对道德 AI 实践的承诺。 XAI 使组织能够理解和解释人工智慧生成决策背后的推理，使决策过程更加透明和负责任。这不仅有助于组织遵守法规，还可以培养利害关係人（包括客户、员工和公众）之间的信任。

处理敏感资料的行业（例如医疗保健和金融）特别依赖 XAI 来确保资料隐私和公平性。 XAI 技术使组织能够识别并减轻人工智慧模型中的偏差，确保决策不受种族、性别或社会经济地位等因素的影响。此外，XAI 使组织能够检测并纠正人工智慧系统中的任何意外后果或错误，从而最大限度地减少对个人或社会的潜在危害。随着监管环境的不断发展，对 XAI 的需求预计将进一步增长。各行业的组织都认识到将其人工智慧系统与法律和道德标准保持一致的重要性。透过采用 XAI，这些组织不仅可以满足合规性要求，还可以透过展示其对负责任的人工智慧实践的承诺来获得竞争优势。随着越来越多的产业在人工智慧部署中优先考虑透明度、公平性和问责制，XAI 市场有望大幅扩张。

改进的决策支持

XAI（可解释人工智慧）是一种强大的工具，可以为人工智慧系统产生的见解提供清晰易懂的解释，从而使企业和专业人士能够增强决策过程。事实证明，这项技术在医疗保健和金融等领域特别有价值，它可以帮助临床医生、分析师和决策者有效地理解和利用人工智慧驱动的资讯。在医疗保健产业，XAI 在支持临床医生理解人工智慧产生的诊断和治疗建议方面发挥着至关重要的作用。透过为人工智慧模型产生的见解提供易于理解的解释，XAI 可以帮助医疗保健专业人员更深入地了解这些建议背后的推理。这反过来又会改善患者护理，因为临床医生可以根据人工智慧驱动的见解做出更明智的决策。 XAI 充当人工智慧系统中使用的复杂演算法和人类决策者之间的桥樑，使医疗保健专业人员能够信任并充分利用人工智慧技术的潜力。同样，在金融领域，XAI 成为分析师和决策者的宝贵工具。随着人工智慧驱动的投资策略越来越多地采用，XAI 有助于理解这些策略背后的推理。透过提供透明且可解释的解释，XAI 使金融专业人士能够清楚地了解人工智慧模型产生的见解。这使他们能够在投资、风险管理和整体投资组合管理方面做出更明智的决策。 XAI 在金融机构中的使用有助于弥合人工智慧模型的复杂性与人类决策者需要清楚了解其基本原理之间的差距。

由于人们认识到 XAI 作为决策支援工具的价值，XAI 市场正在经历显着成长。随着企业和专业人士越来越认识到对人工智慧产生的见解进行可理解的解释的重要性，对 XAI 的需求持续增长。 XAI 弥合复杂人工智慧模型和人类决策者之间差距的能力被视为释放人工智慧技术在各行业的全部潜力的关键因素。透过帮助企业和专业人士做出更明智的决策，XAI 正在推动医疗保健和金融等领域的积极变革并改善成果。

增强使用者信任

人工智慧日益融入我们的日常生活，凸显了建立使用者对人工智慧系统信任的至关重要性。培养这种信任的一种方法是采用可解释的人工智慧（XAI），其目的是使人工智慧系统透明且可解释，从而消除与人工智慧「黑盒子」性质相关的担忧。 XAI 的这一方面对于自动驾驶汽车和关键基础设施等领域尤其重要，因为这些领域的安全性和可靠性至关重要。因此，组织正在认识到 XAI 在增强用户对人工智慧技术的信心方面的重要性，从而导致市场的显着扩张。

在人工智慧日益普及的时代，使用者对人工智慧系统内部运作的担忧是可以理解的。人工智慧传统的「黑盒子」性质，即在没有明确解释的情况下做出决策，引发了人们对这些系统的可靠性、公平性和问责制的质疑。 XAI 透过提供人工智慧系统如何做出决策的见解来解决这些问题，使决策过程对使用者来说更加透明且易于理解。在自动驾驶汽车等领域，人工智慧在确保安全和高效的交通方面发挥着至关重要的作用，用户的信任至关重要。解释人工智慧驱动决策背后的推理能力可以帮助减轻与事故或故障相关的担忧。透过提供清晰的解释，XAI 使用户能够理解为什么做出特定决定，增强他们对技术的信心并培养信任。

同样，在能源、医疗保健和金融等关键基础设施领域，依赖人工智慧系统做出重要决策，XAI 在确保这些系统的安全性和可靠性方面可以发挥至关重要的作用。透过让人工智慧系统变得可解释，组织可以解决与偏见、错误或恶意攻击相关的问题，从而增强使用者对该技术的信任和信心。有鑑于使用者信任对人工智慧系统的重要性，组织正在投资 XAI 以增强对人工智慧技术的信心。这项投资是因为我们认识到用户信任是市场扩张的关键驱动力。透过采用 XAI，组织可以透过提供透明且可解释的 AI 系统来脱颖而出，从而吸引更多用户和客户。

主要市场挑战

对可解释人工智慧的理解有限

全球可解释人工智慧市场面临的主要挑战之一是组织对采用可解释人工智慧解决方案的重要性和好处的理解和认识有限。许多企业可能没有完全理解人工智慧模型解释能力的重要性以及与黑盒演算法相关的潜在风险。这种意识的缺乏可能会导致人们在投资可解释的人工智慧方面犹豫不决，从而使组织容易受到决策偏见、缺乏透明度和监管合规问题等问题的影响。应对这项挑战需要全面的教育倡议，以强调可解释的人工智慧在建立信任、确保公平和实现人工智慧系统的可解释性方面发挥的关键作用。组织需要认识到，可解释的人工智慧可以提供有关人工智慧模型如何做出决策的见解、增强问责制并促进更好的决策过程。现实世界的例子和案例研究展示了可解释的人工智慧的实际好处，有助于加深对其重要性的理解。

实施和整合的复杂性

可解释的人工智慧解决方案的实施和整合可能会给组织带来复杂的挑战，特别是那些技术专业知识或资源有限的组织。有效配置和部署可解释的人工智慧模型，并将其与现有人工智慧系统和工作流程集成，在技术上要求很高。整合过程中可能会出现相容性问题，从而导致延迟和效能不佳。为了应对这些挑战，简化可解释的人工智慧解决方案的部署和管理至关重要。应提供使用者友善的介面和直觉的配置选项，以简化设定和自订。此外，组织应该能够获得全面的支援和指导，包括文件、教程和技术专家，他们可以协助整合和解决任何问题。简化可解释的人工智慧实施的这些方面可以带来更有效率的流程并提高模型的可解释性。

平衡解释能力和绩效。

可解释的人工智慧模型旨在提供透明度和可解释性，但它们面临着在解释能力和性能之间取得适当平衡的挑战。高度可解释的模型可能会牺牲预测准确性，而复杂的模型可能缺乏可解释性。组织需要在模型解释能力和效能之间找到最佳权衡，以确保人工智慧系统既可信又有效。这项挑战需要持续的研究和开发工作，以提高人工智慧模型的可解释性，同时又不影响其效能。先进的技术，例如与模型无关的方法和事后可解释性方法，可以透过提供对模型行为和决策过程的见解来帮助应对这项挑战。努力在这些领域不断改进将使组织能够有效利用可解释的人工智慧，同时保持高效能标准。

监管和道德考虑

全球可解释人工智慧市场也面临着监管合规和道德考量的挑战。随着人工智慧系统在医疗保健、金融和自动驾驶汽车等关键领域变得越来越普遍，对透明度和问责制的需求也越来越大。监管框架正在製定中，以确保人工智慧系统公平、公正且可解释。组织必须应对这些不断变化的法规，并确保其可解释的人工智慧解决方案符合法律和道德标准。这项挑战要求组织随时了解最新的监管动态，并投资于强大的治理框架，以解决潜在的偏见、歧视和隐私问题。行业利益相关者、政策制定者和研究人员之间的合作对于制定促进负责任和合乎道德地使用可解释人工智慧的指南和标准至关重要。

主要市场趋势

对可解释的人工智慧解决方案的需求增加

随着组织认识到人工智慧系统透明度和可解释性的重要性，可解释人工智慧 (XAI) 的全球市场需求激增。随着人工智慧在各行业的日益普及，人们越来越需要了解人工智慧演算法如何做出决策并为其输出提供解释。这项需求是由监管要求、道德考虑以及与最终用户建立信任的需要所驱动的。

可解释的人工智慧解决方案旨在透过提供对人工智慧模型决策过程的洞察来解决「黑盒子」问题。这些解决方案利用基于规则的系统、与模型无关的方法和可解释的机器学习演算法等技术来产生人类易于理解的解释。透过提供清晰的解释，组织可以深入了解影响人工智慧决策的因素，识别潜在的偏见，并确保人工智慧系统的公平性和问责制。

转向针对特定产业的可解释人工智慧解决方案

全球市场正在经历向特定行业的可解释人工智慧解决方案的转变。由于不同行业有独特的需求和挑战，因此需要客製化的 XAI 解决方案来有效解决特定用例。组织正在寻求能够提供与其行业领域（例如医疗保健、金融或製造）相关的解释的 XAI 解决方案。

行业特定的 XAI 解决方案利用领域知识和上下文资讯来产生对最终用户有意义且可操作的解释。这些解决方案使组织能够更深入地了解其特定行业背景下的人工智慧决策流程，从而提高信任、做出更好的决策并增强监管合规性。

人机协作一体化

人机协作的融合是全球可解释人工智慧市场的重要趋势。 XAI 解决方案的目的不是取代人类，而是透过提供可解释的见解和解释来增强人类决策。人类和人工智慧系统之间的这种协作使用户能够理解人工智慧输出背后的推理，并根据这些解释做出明智的决策。

可解释的人工智慧解决方案透过使用视觉化、自然语言解释或互动式介面以使用者友好的方式呈现解释，促进人类与人工智慧的协作。这允许用户与人工智慧系统互动、提出问题并探索不同的场景，以更深入地了解人工智慧生成的输出。透过促进协作，组织可以利用人类和人工智慧系统的优势，从而实现更可靠、更值得信赖的决策过程。

细分市场洞察

最终用途见解

根据最终用途，市场分为医疗保健、BFSI、航空航太和国防、零售和电子商务、公共部门和公用事业、IT 和电信、汽车等。 2022 年，IT 和电信业的收入占比最高，达到 17.99%。5G 和物联网 (IoT) 的推出使组织和个人能够即时收集更多真实世界的资料。人工智慧 (AI) 系统可以利用这些资料变得越来越复杂和强大。

借助电信领域的人工智慧，行动营运商可以增强连接性和客户体验。行动电信商可以利用人工智慧优化和自动化网络，提供更好的服务，让更多人能够连接。例如，AT&T 透过利用人工智慧和统计演算法的预测模型来预测和防止网路服务中断，而 Telenor 使用先进的资料分析来降低其无线电网路中的能源使用和二氧化碳排放。人工智慧系统还可以支援与客户进行更个人化和有意义的互动。

BFSI 中的可解释人工智慧预计将透过提高生产力和降低成本，同时提高向客户提供的服务和商品的品质，为金融组织带来竞争优势。这些竞争优势随后可以透过提供更高品质和更个人化的产品、发布资料洞察来指导投资策略以及透过对信用记录很少的客户进行信用度分析来增强金融普惠性而使金融消费者受益。预计这些因素将促进市场成长。

部署见解

根据部署，市场分为云端和本地。 2022 年，本地细分市场占据最大的收入份额，为 55.73%。使用本地可解释的 AI 可以带来多种好处，例如提高资料安全性、减少延迟以及增强对 AI 系统的控制。此外，对于受到限制使用基于云端的服务的监管要求的组织来说可能更可取。组织使用基于规则的系统、决策树和基于模型的解释等各种技术来实现本地可解释的人工智慧。这些技术可以深入了解人工智慧系统如何做出特定决策或预测，使用户能够验证系统的推理并识别潜在的偏差或错误。

各个垂直行业的主要参与者，尤其是 BFSI、零售业和政府，更喜欢在本地部署 XAI，因为它具有安全优势。例如，金融服务公司摩根大通在本地使用可解释的人工智慧来改善诈欺侦测并防止洗钱。该系统使用机器学习来分析大量资料，识别潜在的诈欺活动，并为其决策提供清晰透明的解释。同样，科技公司 IBM 提供了一个名为 Watson OpenScale 的本地可解释人工智慧平台，该平台可协助组织管理和监控其人工智慧系统的效能和透明度。该平台为人工智慧决策和预测提供了清晰的解释，并允许组织追踪和分析用于训练其人工智慧模型的资料。

应用洞察

根据应用，市场分为诈欺和异常检测、药物发现和诊断、预测性维护、供应链管理、身分和存取管理等。人工智慧 (AI) 在诈欺管理中发挥着至关重要的作用。 2022 年，在诈欺和异常检测领域占据最大收入份额，达到 23.86%。

机器学习 (ML) 演算法是人工智慧的一个组成部分，可以检查大量资料，以识别可能表明诈欺活动的趋势和异常情况。由人工智慧驱动的诈欺管理系统可以侦测并阻止各种欺诈，包括金融诈欺、身分盗窃和网路钓鱼尝试。他们还可以改变并发现新的诈欺模式和趋势，从而提高检测能力。

XAI 在具有预测性维护的製造中的突出应用正在推动市场成长。製造业中的 XAI 预测分析涉及使用可解释的 AI 模型对製造业进行预测并产生见解。可解释的人工智慧技术用于开发预测製造工厂设备故障或维护需求的模型。透过分析历史感测器资料、维护日誌和其他相关信息，XAI 模型可以识别导致设备故障的关键因素，并为预测的维护需求提供可解释的解释。

此外，可解释的人工智慧模型在品质控制过程中利用预测分析。透过分析生产资料、感测器读数和其他相关参数，XAI 模型可以预测製造过程中出现缺陷或偏差的可能性。这些模型还可以解释导致品质问题的因素，帮助製造商了解根本原因并采取纠正措施。

区域洞察

北美在 2022 年占据市场主导地位，份额为 40.52%，预计在预测期内CAGR为 13.4%。德国、法国、美国、英国、日本和加拿大等已开发国家强大的IT基础设施是支持这些国家可解释人工智慧市场成长的主要因素。

推动这些国家可解释人工智慧市场扩张的另一个因素是这些国家政府对更新IT基础设施的大力援助。然而，印度和中国等发展中国家预计在预测期内将出现更高的成长。这些国家良好的经济成长吸引了许多适合扩展可解释人工智慧业务的投资。

预计亚太地区在预测期内将以 24.8% 的最快CAGR成长。亚太国家技术的显着进步正在推动市场成长。例如，2021 年 2 月，日本富士通实验室和北海道大学开发了一种基于「可解释的人工智慧」原则的新系统。它会根据有关资料（例如来自医疗检查的数据）的人工智慧结果，自动向用户显示获得所需结果所需执行的步骤。

目录

第 1 章：产品概述

• 市场定义
• 市场范围
  • 涵盖的市场
  • 考虑学习的年份
  • 主要市场区隔

第 2 章：研究方法

• 研究目的
• 基线方法
• 范围的製定
• 假设和限制
• 研究来源
  • 二次研究
  • 初步研究
• 市场研究方法
  • 自下而上的方法
  • 自上而下的方法
• 计算市场规模和市场份额所遵循的方法
• 预测方法
  • 数据三角测量与验证

第 3 章：执行摘要

第 4 章：COVID-19 对全球可解释人工智慧市场的影响

第 5 章：客户之声

第 6 章：全球可解释人工智慧市场概述

第 7 章：全球可解释人工智慧市场展望

• 市场规模及预测
  • 按价值
• 市占率及预测
  • 按组件（解决方案、服务）
  • 按部署（云端、本机）
  • 按应用（诈欺和异常检测、药物发现和诊断、预测性维护、供应链管理、身分和存取管理、其他）
  • 按最终用途（医疗保健、BFSI、航太和国防、零售和电子商务、公共部门和公用事业、IT 和电信、汽车、其他）
  • 按地区（北美、欧洲、南美、中东和非洲、亚太地区）
• 按公司划分 (2022)
• 市场地图

第 8 章：北美可解释的人工智慧市场前景

• 市场规模及预测
  • 按价值
• 市占率及预测
  • 按组件
  • 按部署
  • 按应用
  • 按最终用途
  • 按国家/地区
• 北美：国家分析
  • 美国
  • 加拿大
  • 墨西哥

第 9 章：欧洲可解释的人工智慧市场前景

• 市场规模及预测
  • 按价值
• 市占率及预测
  • 按组件
  • 按部署
  • 按应用
  • 按最终用途
  • 按国家/地区
• 欧洲：国家分析
  • 德国
  • 法国
  • 英国
  • 义大利
  • 西班牙
  • 比利时

第 10 章：南美洲可解释的人工智慧市场前景

• 市场规模及预测
  • 按价值
• 市占率及预测
  • 按组件
  • 按部署
  • 按应用
  • 按最终用途
  • 按国家/地区
• 南美洲：国家分析
  • 巴西
  • 哥伦比亚
  • 阿根廷
  • 智利
  • 秘鲁

第 11 章：中东和非洲可解释的人工智慧市场前景

• 市场规模及预测
  • 按价值
• 市占率及预测
  • 按组件
  • 按部署
  • 按应用
  • 按最终用途
  • 按国家/地区
• 中东和非洲：国家分析
  • 沙乌地阿拉伯
  • 阿联酋
  • 南非
  • 土耳其
  • 以色列

第 12 章：亚太地区可解释的人工智慧市场展望

• 市场规模及预测
  • 按组件
  • 按部署
  • 按应用
  • 按最终用途
  • 按国家/地区
• 亚太地区：国家分析
  • 中国可解释的人工智慧
  • 印度可解释的人工智慧
  • 日本可解释的人工智慧
  • 韩国可解释的人工智慧
  • 澳洲可解释的人工智慧
  • 印尼可解释的人工智慧
  • 越南可解释的人工智慧

第 13 章：市场动态

• 司机
• 挑战

第 14 章：市场趋势与发展

第 15 章：公司简介

• 阿米莉亚美国有限责任公司
  • Business Overview
  • Key Revenue and Financials
  • Recent Developments
  • Key Personnel/Key Contact Person
  • Key Product/Services Offered
• 建构组
  • Business Overview
  • Key Revenue and Financials
  • Recent Developments
  • Key Personnel/Key Contact Person
  • Key Product/Services Offered
• 数据机器人公司
  • Business Overview
  • Key Revenue and Financials
  • Recent Developments
  • Key Personnel/Key Contact Person
  • Key Product/Services Offered
• 同上.ai
  • Business Overview
  • Key Revenue and Financials
  • Recent Developments
  • Key Personnel/Key Contact Person
  • Key Product/Services Offered
• 达尔文人工智慧
  • Business Overview
  • Key Revenue and Financials
  • Recent Developments
  • Key Personnel/Key Contact Person
  • Key Product/Services Offered
• 事实资料
  • Business Overview
  • Key Revenue and Financials
  • Recent Developments
  • Key Personnel/Key Contact Person
  • Key Product/Services Offered
• 谷歌有限责任公司
  • Business Overview
  • Key Revenue and Financials
  • Recent Developments
  • Key Personnel/Key Contact Person
  • Key Product/Services Offered
• IBM公司
  • Business Overview
  • Key Revenue and Financials
  • Recent Developments
  • Key Personnel/Key Contact Person
  • Key Product/Services Offered
• 肯迪
  • Business Overview
  • Key Revenue and Financials
  • Recent Developments
  • Key Personnel/Key Contact Person
  • Key Product/Services Offered
• 微软公司
  • Business Overview
  • Key Revenue and Financials
  • Recent Developments
  • Key Personnel/Key Contact Person
  • Key Product/Services Offered

第 16 章：策略建议

第 17 章：关于我们与免责声明

Global Explainable AI Market has valued at USD 5.4 Billion in 2022 and is anticipated to project robust growth in the forecast period with a CAGR of 22.4% through 2028. The Global Explainable AI (XAI) Market is experiencing significant growth as organizations increasingly adopt artificial intelligence solutions across various industries. XAI refers to the capability of AI systems to provide understandable and interpretable explanations for their decisions and actions, addressing the "black box" challenge of traditional AI. The market is poised for expansion, driven by the growing need for transparency, accountability, and ethical AI deployment. XAI is vital in sectors such as finance, healthcare, and autonomous vehicles, where the ability to understand AI-generated decisions is crucial for regulatory compliance and user trust. Additionally, the rise of AI-related regulations and guidelines further propels the demand for XAI solutions. The market is characterized by innovations in machine learning techniques, algorithms, and model architectures that enhance the interpretability of AI systems. As businesses prioritize responsible AI practices, the Explainable AI Market is set to continue its growth trajectory, offering solutions that not only deliver AI-driven insights but also ensure transparency and human-centric AI decision-making processes.

Key Market Drivers

Transparency in Decision-Making

Market Overview
Forecast Period	2024-2028
Market Size 2022	USD 5.4 Billion
Market Size 2028	USD 18.32 billion
CAGR 2023-2028	22.4%
Fastest Growing Segment	Cloud
Largest Market	North America

The Global Explainable AI (XAI) Market is witnessing significant growth as a result of the growing demand for transparency and interpretability in artificial intelligence (AI) systems. XAI plays a crucial role in various sectors, including healthcare, finance, and autonomous vehicles, where comprehending the decisions made by AI systems is vital for regulatory compliance and user trust. With the increasing adoption of AI, there is a corresponding need to unravel the complexities of AI models and algorithms, making XAI solutions increasingly indispensable. The market thrives on continuous innovations in machine learning techniques and algorithms that enhance the interpretability of AI systems, ensuring that organizations can leverage the power of AI while upholding accountability and ethical AI practices.

The rising demand for transparency and interpretability in AI systems is a key driver behind the robust growth of the Global XAI Market. As AI becomes more prevalent in various industries, there is a growing need to understand the decision-making processes of AI systems. This is particularly crucial in sectors such as healthcare, where AI is used to make critical diagnoses and treatment recommendations. By providing explanations for AI-driven decisions, XAI enables healthcare professionals to trust and validate the outcomes, ensuring regulatory compliance and patient safety. Similarly, in the finance sector, where AI is employed for tasks like fraud detection and risk assessment, XAI plays a pivotal role in ensuring transparency and accountability. Financial institutions need to understand the reasoning behind AI-driven decisions to comply with regulations and maintain customer trust. XAI solutions provide insights into the inner workings of AI models, enabling organizations to explain and justify their decisions to regulators, auditors, and customers.

Autonomous vehicles are another area where XAI is of utmost importance. As self-driving cars become more prevalent, it is crucial to understand the decision-making processes of AI algorithms that control these vehicles. XAI allows manufacturers and regulators to comprehend the reasoning behind AI-driven actions, ensuring safety, reliability, and compliance with regulations. The continuous advancements in machine learning techniques and algorithms are driving the growth of the XAI market. Researchers and developers are constantly working on innovative approaches to enhance the interpretability of AI systems. These advancements include techniques such as rule extraction, feature importance analysis, and model-agnostic explanations. By making AI models more transparent and understandable, organizations can address concerns related to bias, fairness, and accountability, fostering trust and ethical AI practices.

Regulatory Compliance

The global market for Explainable Artificial Intelligence (XAI) is experiencing significant growth due to the increasing number of regulations and guidelines related to AI. Governments and industry watchdogs are placing a strong emphasis on ethical AI practices, which is compelling organizations to adopt XAI solutions to meet compliance requirements. As regulatory frameworks continue to evolve, XAI plays a crucial role in helping organizations ensure that their AI systems adhere to legal and ethical standards. This growing demand for XAI, driven by regulatory requirements, is particularly prominent in industries where data privacy, fairness, and accountability are of utmost importance. The surge in AI-related regulations and guidelines worldwide has created a favorable environment for the XAI market to thrive. Governments and regulatory bodies are recognizing the potential risks associated with AI systems that lack transparency and interpretability. As a result, they are implementing measures to ensure that AI technologies are developed and deployed responsibly. These regulations often require organizations to provide explanations for the decisions made by their AI systems, especially in critical domains such as healthcare, finance, and criminal justice. By adopting XAI solutions, organizations can address these regulatory requirements and demonstrate their commitment to ethical AI practices. XAI enables organizations to understand and explain the reasoning behind AI-generated decisions, making the decision-making process more transparent and accountable. This not only helps organizations comply with regulations but also fosters trust among stakeholders, including customers, employees, and the public.

Industries that handle sensitive data, such as healthcare and finance, are particularly reliant on XAI to ensure data privacy and fairness. XAI techniques allow organizations to identify and mitigate biases in AI models, ensuring that decisions are not influenced by factors such as race, gender, or socioeconomic status. Moreover, XAI enables organizations to detect and rectify any unintended consequences or errors in AI systems, thereby minimizing potential harm to individuals or society. As the regulatory landscape continues to evolve, the demand for XAI is expected to grow further. Organizations across various sectors are recognizing the importance of aligning their AI systems with legal and ethical standards. By embracing XAI, these organizations can not only meet compliance requirements but also gain a competitive edge by demonstrating their commitment to responsible AI practices. The XAI market is poised for significant expansion as more industries prioritize transparency, fairness, and accountability in their AI deployments.

Improved Decision Support

XAI, or Explainable Artificial Intelligence, is a powerful tool that enables businesses and professionals to enhance their decision-making processes by offering clear and understandable explanations for insights generated by AI systems. This technology has proven particularly valuable in sectors such as healthcare and finance, where it assists clinicians, analysts, and decision-makers in comprehending and utilizing AI-driven information effectively. In the healthcare industry, XAI plays a crucial role in supporting clinicians in understanding AI-generated diagnoses and treatment recommendations. By providing comprehensible explanations for the insights produced by AI models, XAI helps healthcare professionals gain a deeper understanding of the reasoning behind these recommendations. This, in turn, leads to improved patient care as clinicians can make more informed decisions based on the AI-driven insights. XAI acts as a bridge between the complex algorithms used in AI systems and the human decision-makers, empowering healthcare professionals to trust and utilize AI technology to its fullest potential. Similarly, in the financial sector, XAI serves as a valuable tool for analysts and decision-makers. With the increasing adoption of AI-driven investment strategies, XAI aids in comprehending the reasoning behind these strategies. By providing transparent and interpretable explanations, XAI enables financial professionals to have a clear understanding of the insights generated by AI models. This empowers them to make better-informed decisions regarding investments, risk management, and overall portfolio management. The use of XAI in financial institutions helps bridge the gap between the complexity of AI models and the need for human decision-makers to have a clear understanding of the underlying rationale.

The market for XAI is experiencing significant growth due to the recognition of its value as a decision-support tool. As businesses and professionals increasingly understand the importance of comprehensible explanations for AI-generated insights, the demand for XAI continues to rise. XAI's ability to bridge the gap between complex AI models and human decision-makers is seen as a crucial factor in unlocking the full potential of AI technology across various industries. By empowering businesses and professionals to make better-informed decisions, XAI is driving positive change and improving outcomes in sectors such as healthcare and finance.

Enhanced User Trust

The increasing integration of AI into our everyday lives highlights the crucial importance of establishing user trust in AI systems. One approach to fostering this trust is through the adoption of Explainable AI (XAI), which aims to make AI systems transparent and explainable, thereby dispelling concerns associated with the "black box" nature of AI. This aspect of XAI is particularly vital in sectors such as autonomous vehicles and critical infrastructure, where safety and reliability are of utmost importance. As a result, organizations are recognizing the significance of XAI in bolstering user confidence in AI technologies, leading to a significant expansion of the market.

In an era where AI is becoming increasingly pervasive, users are understandably concerned about the inner workings of AI systems. The traditional "black box" nature of AI, where decisions are made without clear explanations, has raised questions about the reliability, fairness, and accountability of these systems. XAI addresses these concerns by providing insights into how AI systems arrive at their decisions, making the decision-making process more transparent and understandable to users. In sectors like autonomous vehicles, where AI plays a crucial role in ensuring safe and efficient transportation, user trust is paramount. The ability to explain the reasoning behind AI-driven decisions can help alleviate concerns related to accidents or malfunctions. By providing clear explanations, XAI enables users to understand why a particular decision was made, increasing their confidence in the technology, and fostering trust.

Similarly, in critical infrastructure sectors such as energy, healthcare, and finance, where AI systems are relied upon for making important decisions, XAI can play a vital role in ensuring the safety and reliability of these systems. By making AI systems explainable, organizations can address concerns related to biases, errors, or malicious attacks, thereby enhancing user trust and confidence in the technology. Recognizing the significance of user trust in AI systems, organizations are investing in XAI to bolster confidence in AI technologies. This investment is driven by the understanding that user trust is a key driver for market expansion. By adopting XAI, organizations can differentiate themselves by offering transparent and explainable AI systems, which in turn can attract more users and customers.

Key Market Challenges

Limited Understanding of Explainable AI

One of the primary challenges facing the global explainable AI market is the limited understanding and awareness among organizations regarding the importance and benefits of adopting explainable AI solutions. Many businesses may not fully grasp the significance of explaining ability in AI models and the potential risks associated with black-box algorithms. This lack of awareness can lead to hesitation in investing in explainable AI, leaving organizations vulnerable to issues such as biased decision-making, lack of transparency, and regulatory compliance concerns. Addressing this challenge requires comprehensive educational initiatives to highlight the critical role that explainable AI plays in building trust, ensuring fairness, and enabling interpretability in AI systems. Organizations need to recognize that explainable AI can provide insights into how AI models make decisions, enhance accountability, and facilitate better decision-making processes. Real-world examples and case studies showcasing the tangible benefits of explainable AI can help foster a deeper understanding of its significance.

Complexity of Implementation and Integration

The implementation and integration of explainable AI solutions can pose complex challenges for organizations, particularly those with limited technical expertise or resources. Configuring and deploying explainable AI models effectively, and integrating them with existing AI systems and workflows, can be technically demanding. Compatibility issues may arise during integration, leading to delays and suboptimal performance. To address these challenges, it is crucial to simplify the deployment and management of explainable AI solutions. User-friendly interfaces and intuitive configuration options should be provided to streamline setup and customization. Additionally, organizations should have access to comprehensive support and guidance, including documentation, tutorials, and technical experts who can assist with integration and troubleshoot any issues. Simplifying these aspects of explainable AI implementation can lead to more efficient processes and improved model interpretability.

Balancing Explain ability and Performance.

Explainable AI models aim to provide transparency and interpretability, but they face the challenge of striking the right balance between explain ability and performance. Highly interpretable models may sacrifice predictive accuracy, while complex models may lack interpretability. Organizations need to find the optimal trade-off between model explain ability and performance to ensure that AI systems are both trustworthy and effective. This challenge requires ongoing research and development efforts to improve the interpretability of AI models without compromising their performance. Advanced techniques, such as model-agnostic approaches and post-hoc interpretability methods, can help address this challenge by providing insights into model behavior and decision-making processes. Striving for continuous improvement in these areas will enable organizations to leverage explainable AI effectively while maintaining high-performance standards.

Regulatory and Ethical Considerations

The global explainable AI market also faces challenges related to regulatory compliance and ethical considerations. As AI systems become more prevalent in critical domains such as healthcare, finance, and autonomous vehicles, there is a growing need for transparency and accountability. Regulatory frameworks are being developed to ensure that AI systems are fair, unbiased, and explainable. Organizations must navigate these evolving regulations and ensure that their explainable AI solutions comply with legal and ethical standards. This challenge requires organizations to stay updated with the latest regulatory developments and invest in robust governance frameworks to address potential biases, discrimination, and privacy concerns. Collaboration between industry stakeholders, policymakers, and researchers is essential to establish guidelines and standards that promote responsible and ethical use of explainable AI.

Key Market Trends

Rise in Demand for Explainable AI Solutions

The global market for Explainable AI (XAI) is witnessing a surge in demand as organizations recognize the importance of transparency and interpretability in AI systems. With the increasing adoption of AI across various industries, there is a growing need to understand how AI algorithms make decisions and provide explanations for their outputs. This demand is driven by regulatory requirements, ethical considerations, and the need to build trust with end-users.

Explainable AI solutions aim to address the "black box" problem by providing insights into the decision-making process of AI models. These solutions utilize techniques such as rule-based systems, model-agnostic approaches, and interpretable machine learning algorithms to generate explanations that can be easily understood by humans. By providing clear explanations, organizations can gain valuable insights into the factors influencing AI decisions, identify potential biases, and ensure fairness and accountability in AI systems.

Shift towards Industry-Specific Explainable AI Solutions

The global market is experiencing a shift towards industry-specific Explainable AI solutions. As different industries have unique requirements and challenges, there is a need for tailored XAI solutions that can address specific use cases effectively. Organizations are seeking XAI solutions that can provide explanations relevant to their industry domain, such as healthcare, finance, or manufacturing.

Industry-specific XAI solutions leverage domain knowledge and contextual information to generate explanations that are meaningful and actionable for end-users. These solutions enable organizations to gain deeper insights into AI decision-making processes within their specific industry context, leading to improved trust, better decision-making, and enhanced regulatory compliance.

Integration of Human-AI Collaboration

The integration of human-AI collaboration is a significant trend in the global Explainable AI market. Rather than replacing humans, XAI solutions aim to augment human decision-making by providing interpretable insights and explanations. This collaboration between humans and AI systems enables users to understand the reasoning behind AI outputs and make informed decisions based on those explanations.

Explainable AI solutions facilitate human-AI collaboration by presenting explanations in a user-friendly manner, using visualizations, natural language explanations, or interactive interfaces. This allows users to interact with AI systems, ask questions, and explore different scenarios to gain a deeper understanding of AI-generated outputs. By fostering collaboration, organizations can leverage the strengths of both humans and AI systems, leading to more reliable and trustworthy decision-making processes.

Segmental Insights

End-use Insights

Based on end-use, the market is segmented into healthcare, BFSI, aerospace & defense, retail and e-commerce, public sector & utilities, it & telecommunication, automotive, and others. IT & telecommunication sector accounted for the highest revenue share of 17.99% in 2022. The rollout of 5G and the Internet of Things (IoT) is enabling organizations and individuals to collect more real-world data in real time. Artificial intelligence (AI) systems can use this data to become increasingly sophisticated and capable.

Mobile carriers can enhance connectivity and their customers' experiences thanks to AI in the telecom sector. Mobile operators can offer better services and enable more people to connect by utilizing AI to optimize and automate networks. For instance, While AT&T anticipates and prevents network service interruptions by utilizing predictive models that use AI and statistical algorithms, Telenor uses advanced data analytics to lower energy usage and CO2 emissions in its radio networks. AI systems can also support more personalized and meaningful interactions with customers.

Explainable AI in BFSI is anticipated to give financial organizations a competitive edge by increasing their productivity and lowering costs while raising the quality of the services and goods they provide to customers. These competitive advantages can subsequently benefit financial consumers by delivering higher-quality and more individualized products, releasing data insights to guide investment strategies, and enhancing financial inclusion by enabling the creditworthiness analysis of customers with little credit history. These factors are anticipated to augment the market growth.

Deployment Insights

Based on deployment, the market is segmented into cloud and on-premises. The on-premises segment held the largest revenue share of 55.73% in 2022. Using on-premises explainable AI can provide several benefits, such as improved data security, reduced latency, and increased control over the AI system. Additionally, it may be preferable for organizations subject to regulatory requirements limiting the use of cloud-based services. Organizations use various techniques such as rule-based systems, decision trees, and model-based explanations to implement on-premises explainable AI. These techniques provide insights into how the AI system arrived at a particular decision or prediction, allowing users to verify the system's reasoning and identify potential biases or errors.

Major players across various industry verticals, especially in the BFSI, retail, and government, prefer XAI deployed on-premises, owing to its security benefits. For instance, the financial services company JP Morgan uses explainable AI on-premises to improve fraud detection and prevent money laundering. The system uses machine learning to analyze large volumes of data, identify potentially fraudulent activities, and provide clear and transparent explanations for its decisions. Similarly, IBM, the technology company, provides an on-premises explainable AI platform termed Watson OpenScale, which helps organizations manage and monitor the performance and transparency of their AI systems. The platform provides clear explanations for AI decisions and predictions and allows organizations to track and analyze the data used to train their AI models.

Application Insights

Based on application, the market is segmented into fraud and anomaly detection, drug discovery & diagnostics, predictive maintenance, supply chain management, identity and access management, and others. Artificial intelligence (AI) plays a crucial role in fraud management. The fraud and anomaly detection segment accounted for the largest revenue share of 23.86% in 2022.

Machine Learning (ML) algorithms, a component of AI, can examine enormous amounts of data to identify trends and anomalies that could indicate fraudulent activity. Systems for managing fraud powered by AI can detect and stop various frauds, including financial fraud, identity theft, and phishing attempts. They can also change and pick up on new fraud patterns and trends, thereby increasing their detection.

The prominent use of XAI in manufacturing with predictive maintenance is propelling the market growth. XAI predictive analysis in manufacturing involves using interpretable AI models to make predictions and generate insights in the manufacturing industry. Explainable AI techniques are used to develop models that predict equipment failures or maintenance needs in manufacturing plants. By analyzing historical sensor data, maintenance logs, and other relevant information, XAI models identify the key factors contributing to equipment failures and provide interpretable explanations for the predicted maintenance requirements.

Moreover, explainable AI models leverage predictive analysis in quality control processes. By analyzing production data, sensor readings, and other relevant parameters, XAI models can predict the likelihood of defects or deviations in manufacturing processes. The models can also provide explanations for the factors contributing to quality issues, helping manufacturers understand the root causes and take corrective actions.

Regional Insights

North America dominated the market with a share of 40.52% in 2022 and is projected to grow at a CAGR of 13.4% over the forecast period. Strong IT infrastructure in developed nations such as Germany, France, the U.S., the UK, Japan, and Canada is a major factor supporting the growth of the explainable AI market in these countries.

Another factor driving the market expansion of explainable AI in these countries is the substantial assistance from the governments of these nations to update the IT infrastructure. However, developing nations like India and China are expected to display higher growth during the forecast period. Numerous investments that are appropriate for the expansion of the explainable AI business are drawn to these nations by their favorable economic growth.

Asia Pacific is anticipated to grow at the fastest CAGR of 24.8% during the forecast period. Significant advancements in technology in Asia Pacific countries are driving market growth. For instance, in February 2021, a new system built on the 'explainable AI' principle was developed by Fujitsu Laboratories and Hokkaido University in Japan. It automatically shows users the steps they need to do to obtain a desired result based on AI results about data, such as those from medical exams.

Key Market Players

• Amelia US LLC

• BuildGroup

• DataRobot, Inc.

• Ditto.ai

• DarwinAI

• Factmata

• Google LLC

• IBM Corporation

• Kyndi

• Microsoft Corporation

Report Scope:

In this report, the Global Explainable AI Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Explainable AI Market, By Component:

• Solution
• Services

Explainable AI Market, By Deployment:

• Cloud
• On-premise

Explainable AI Market, By End- use:

• Healthcare
• BFSI
• Aerospace & defense
• Retail and e-commerce
• Public sector & utilities
• IT & telecommunication
• Automotive
• Others

Explainable AI Market, By Application:

• Fraud & Anomaly Detection
• Drug Discovery & Diagnostics
• Predictive Maintenance
• Supply chain management
• Identity and access management
• Others

Explainable AI Market, By Region:

• North America
• United States
• Canada
• Mexico
• Europe
• France
• United Kingdom
• Italy
• Germany
• Spain
• Belgium
• Asia-Pacific
• China
• India
• Japan
• Australia
• South Korea
• Indonesia
• Vietnam
• South America
• Brazil
• Argentina
• Colombia
• Chile
• Peru
• Middle East & Africa
• South Africa
• Saudi Arabia
• UAE
• Turkey
• Israel

Competitive Landscape

• Company Profiles: Detailed analysis of the major companies present in the Global Explainable AI Market.

Available Customizations:

• Global Explainable AI market report with the given market data, Tech Sci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

• Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

• 1.1. Market Definition
• 1.2. Scope of the Market
  • 1.2.1. Markets Covered
  • 1.2.2. Years Considered for Study
  • 1.2.3. Key Market Segmentations

2. Research Methodology

• 2.1. Objective of the Study
• 2.2. Baseline Methodology
• 2.3. Formulation of the Scope
• 2.4. Assumptions and Limitations
• 2.5. Sources of Research
  • 2.5.1. Secondary Research
  • 2.5.2. Primary Research
• 2.6. Approach for the Market Study
  • 2.6.1. The Bottom-Up Approach
  • 2.6.2. The Top-Down Approach
• 2.7. Methodology Followed for Calculation of Market Size & Market Shares
• 2.8. Forecasting Methodology
  • 2.8.1. Data Triangulation & Validation

3. Executive Summary

4. Impact of COVID-19 on Global Explainable AI Market

5. Voice of Customer

6. Global Explainable AI Market Overview

7. Global Explainable AI Market Outlook

• 7.1. Market Size & Forecast
  • 7.1.1. By Value
• 7.2. Market Share & Forecast
  • 7.2.1. By Component (Solution, Services)
  • 7.2.2. By Deployment (Cloud, On-Premises)
  • 7.2.3. By Application (Fraud & Anomaly Detection, Drug Discovery & Diagnostics, Predictive Maintenance, Supply chain management, Identity and access management, Others)
  • 7.2.4. By End-use (Healthcare, BFSI, Aerospace & defense, Retail and e-commerce, Public sector & utilities, IT & telecommunication, Automotive, Others)
  • 7.2.5. By Region (North America, Europe, South America, Middle East & Africa, Asia Pacific)
• 7.3. By Company (2022)
• 7.4. Market Map

8. North America Explainable AI Market Outlook

• 8.1. Market Size & Forecast
  • 8.1.1. By Value
• 8.2. Market Share & Forecast
  • 8.2.1. By Component
  • 8.2.2. By Deployment
  • 8.2.3. By Application
  • 8.2.4. By End-use
  • 8.2.5. By Country
• 8.3. North America: Country Analysis
  • 8.3.1. United States Explainable AI Market Outlook
    • 8.3.1.1. Market Size & Forecast
      • 8.3.1.1.1. By Value
    • 8.3.1.2. Market Share & Forecast
      • 8.3.1.2.1. By Component
      • 8.3.1.2.2. By Deployment
      • 8.3.1.2.3. By Application
      • 8.3.1.2.4. By End-use
  • 8.3.2. Canada Explainable AI Market Outlook
    • 8.3.2.1. Market Size & Forecast
      • 8.3.2.1.1. By Value
    • 8.3.2.2. Market Share & Forecast
      • 8.3.2.2.1. By Component
      • 8.3.2.2.2. By Deployment
      • 8.3.2.2.3. By Application
      • 8.3.2.2.4. By End-use
  • 8.3.3. Mexico Explainable AI Market Outlook
    • 8.3.3.1. Market Size & Forecast
      • 8.3.3.1.1. By Value
    • 8.3.3.2. Market Share & Forecast
      • 8.3.3.2.1. By Component
      • 8.3.3.2.2. By Deployment
      • 8.3.3.2.3. By Application
      • 8.3.3.2.4. By End-use

9. Europe Explainable AI Market Outlook

• 9.1. Market Size & Forecast
  • 9.1.1. By Value
• 9.2. Market Share & Forecast
  • 9.2.1. By Component
  • 9.2.2. By Deployment
  • 9.2.3. By Application
  • 9.2.4. By End-use
  • 9.2.5. By Country
• 9.3. Europe: Country Analysis
  • 9.3.1. Germany Explainable AI Market Outlook
    • 9.3.1.1. Market Size & Forecast
      • 9.3.1.1.1. By Value
    • 9.3.1.2. Market Share & Forecast
      • 9.3.1.2.1. By Component
      • 9.3.1.2.2. By Deployment
      • 9.3.1.2.3. By Application
      • 9.3.1.2.4. By End-use
  • 9.3.2. France Explainable AI Market Outlook
    • 9.3.2.1. Market Size & Forecast
      • 9.3.2.1.1. By Value
    • 9.3.2.2. Market Share & Forecast
      • 9.3.2.2.1. By Component
      • 9.3.2.2.2. By Deployment
      • 9.3.2.2.3. By Application
      • 9.3.2.2.4. By End-use
  • 9.3.3. United Kingdom Explainable AI Market Outlook
    • 9.3.3.1. Market Size & Forecast
      • 9.3.3.1.1. By Value
    • 9.3.3.2. Market Share & Forecast
      • 9.3.3.2.1. By Component
      • 9.3.3.2.2. By Deployment
      • 9.3.3.2.3. By Application
      • 9.3.3.2.4. By End-use
  • 9.3.4. Italy Explainable AI Market Outlook
    • 9.3.4.1. Market Size & Forecast
      • 9.3.4.1.1. By Value
    • 9.3.4.2. Market Share & Forecast
      • 9.3.4.2.1. By Component
      • 9.3.4.2.2. By Deployment
      • 9.3.4.2.3. By Application
      • 9.3.4.2.4. By End-use
  • 9.3.5. Spain Explainable AI Market Outlook
    • 9.3.5.1. Market Size & Forecast
      • 9.3.5.1.1. By Value
    • 9.3.5.2. Market Share & Forecast
      • 9.3.5.2.1. By Component
      • 9.3.5.2.2. By Deployment
      • 9.3.5.2.3. By Application
      • 9.3.5.2.4. By End-use
  • 9.3.6. Belgium Explainable AI Market Outlook
    • 9.3.6.1. Market Size & Forecast
      • 9.3.6.1.1. By Value
    • 9.3.6.2. Market Share & Forecast
      • 9.3.6.2.1. By Component
      • 9.3.6.2.2. By Deployment
      • 9.3.6.2.3. By Application
      • 9.3.6.2.4. By End-use

10. South America Explainable AI Market Outlook

• 10.1. Market Size & Forecast
  • 10.1.1. By Value
• 10.2. Market Share & Forecast
  • 10.2.1. By Component
  • 10.2.2. By Deployment
  • 10.2.3. By Application
  • 10.2.4. By End-use
  • 10.2.5. By Country
• 10.3. South America: Country Analysis
  • 10.3.1. Brazil Explainable AI Market Outlook
    • 10.3.1.1. Market Size & Forecast
      • 10.3.1.1.1. By Value
    • 10.3.1.2. Market Share & Forecast
      • 10.3.1.2.1. By Component
      • 10.3.1.2.2. By Deployment
      • 10.3.1.2.3. By Application
      • 10.3.1.2.4. By End-use
  • 10.3.2. Colombia Explainable AI Market Outlook
    • 10.3.2.1. Market Size & Forecast
      • 10.3.2.1.1. By Value
    • 10.3.2.2. Market Share & Forecast
      • 10.3.2.2.1. By Component
      • 10.3.2.2.2. By Deployment
      • 10.3.2.2.3. By Application
      • 10.3.2.2.4. By End-use
  • 10.3.3. Argentina Explainable AI Market Outlook
    • 10.3.3.1. Market Size & Forecast
      • 10.3.3.1.1. By Value
    • 10.3.3.2. Market Share & Forecast
      • 10.3.3.2.1. By Component
      • 10.3.3.2.2. By Deployment
      • 10.3.3.2.3. By Application
      • 10.3.3.2.4. By End-use
  • 10.3.4. Chile Explainable AI Market Outlook
    • 10.3.4.1. Market Size & Forecast
      • 10.3.4.1.1. By Value
    • 10.3.4.2. Market Share & Forecast
      • 10.3.4.2.1. By Component
      • 10.3.4.2.2. By Deployment
      • 10.3.4.2.3. By Application
      • 10.3.4.2.4. By End-use
  • 10.3.5. Peru Explainable AI Market Outlook
    • 10.3.5.1. Market Size & Forecast
      • 10.3.5.1.1. By Value
    • 10.3.5.2. Market Share & Forecast
      • 10.3.5.2.1. By Component
      • 10.3.5.2.2. By Deployment
      • 10.3.5.2.3. By Application
      • 10.3.5.2.4. By End-use

11. Middle East & Africa Explainable AI Market Outlook

• 11.1. Market Size & Forecast
  • 11.1.1. By Value
• 11.2. Market Share & Forecast
  • 11.2.1. By Component
  • 11.2.2. By Deployment
  • 11.2.3. By Application
  • 11.2.4. By End-use
  • 11.2.5. By Country
• 11.3. Middle East & Africa: Country Analysis
  • 11.3.1. Saudi Arabia Explainable AI Market Outlook
    • 11.3.1.1. Market Size & Forecast
      • 11.3.1.1.1. By Value
    • 11.3.1.2. Market Share & Forecast
      • 11.3.1.2.1. By Component
      • 11.3.1.2.2. By Deployment
      • 11.3.1.2.3. By Application
      • 11.3.1.2.4. By End-use
  • 11.3.2. UAE Explainable AI Market Outlook
    • 11.3.2.1. Market Size & Forecast
      • 11.3.2.1.1. By Value
    • 11.3.2.2. Market Share & Forecast
      • 11.3.2.2.1. By Component
      • 11.3.2.2.2. By Deployment
      • 11.3.2.2.3. By Application
      • 11.3.2.2.4. By End-use
  • 11.3.3. South Africa Explainable AI Market Outlook
    • 11.3.3.1. Market Size & Forecast
      • 11.3.3.1.1. By Value
    • 11.3.3.2. Market Share & Forecast
      • 11.3.3.2.1. By Component
      • 11.3.3.2.2. By Deployment
      • 11.3.3.2.3. By Application
      • 11.3.3.2.4. By End-use
  • 11.3.4. Turkey Explainable AI Market Outlook
    • 11.3.4.1. Market Size & Forecast
      • 11.3.4.1.1. By Value
    • 11.3.4.2. Market Share & Forecast
      • 11.3.4.2.1. By Component
      • 11.3.4.2.2. By Deployment
      • 11.3.4.2.3. By Application
      • 11.3.4.2.4. By End-use
  • 11.3.5. Israel Explainable AI Market Outlook
    • 11.3.5.1. Market Size & Forecast
      • 11.3.5.1.1. By Value
    • 11.3.5.2. Market Share & Forecast
      • 11.3.5.2.1. By Component
      • 11.3.5.2.2. By Deployment
      • 11.3.5.2.3. By Application
      • 11.3.5.2.4. By End-use

12. Asia Pacific Explainable AI Market Outlook

• 12.1. Market Size & Forecast
  • 12.1.1. By Component
  • 12.1.2. By Deployment
  • 12.1.3. By Application
  • 12.1.4. By End-use
  • 12.1.5. By Country
• 12.2. Asia-Pacific: Country Analysis
  • 12.2.1. China Explainable AI Market Outlook
    • 12.2.1.1. Market Size & Forecast
      • 12.2.1.1.1. By Value
    • 12.2.1.2. Market Share & Forecast
      • 12.2.1.2.1. By Component
      • 12.2.1.2.2. By Deployment
      • 12.2.1.2.3. By Application
      • 12.2.1.2.4. By End-use
  • 12.2.2. India Explainable AI Market Outlook
    • 12.2.2.1. Market Size & Forecast
      • 12.2.2.1.1. By Value
    • 12.2.2.2. Market Share & Forecast
      • 12.2.2.2.1. By Component
      • 12.2.2.2.2. By Deployment
      • 12.2.2.2.3. By Application
      • 12.2.2.2.4. By End-use
  • 12.2.3. Japan Explainable AI Market Outlook
    • 12.2.3.1. Market Size & Forecast
      • 12.2.3.1.1. By Value
    • 12.2.3.2. Market Share & Forecast
      • 12.2.3.2.1. By Component
      • 12.2.3.2.2. By Deployment
      • 12.2.3.2.3. By Application
      • 12.2.3.2.4. By End-use
  • 12.2.4. South Korea Explainable AI Market Outlook
    • 12.2.4.1. Market Size & Forecast
      • 12.2.4.1.1. By Value
    • 12.2.4.2. Market Share & Forecast
      • 12.2.4.2.1. By Component
      • 12.2.4.2.2. By Deployment
      • 12.2.4.2.3. By Application
      • 12.2.4.2.4. By End-use
  • 12.2.5. Australia Explainable AI Market Outlook
    • 12.2.5.1. Market Size & Forecast
      • 12.2.5.1.1. By Value
    • 12.2.5.2. Market Share & Forecast
      • 12.2.5.2.1. By Component
      • 12.2.5.2.2. By Deployment
      • 12.2.5.2.3. By Application
      • 12.2.5.2.4. By End-use
  • 12.2.6. Indonesia Explainable AI Market Outlook
    • 12.2.6.1. Market Size & Forecast
      • 12.2.6.1.1. By Value
    • 12.2.6.2. Market Share & Forecast
      • 12.2.6.2.1. By Component
      • 12.2.6.2.2. By Deployment
      • 12.2.6.2.3. By Application
      • 12.2.6.2.4. By End-use
  • 12.2.7. Vietnam Explainable AI Market Outlook
    • 12.2.7.1. Market Size & Forecast
      • 12.2.7.1.1. By Value
    • 12.2.7.2. Market Share & Forecast
      • 12.2.7.2.1. By Component
      • 12.2.7.2.2. By Deployment
      • 12.2.7.2.3. By Application
      • 12.2.7.2.4. By End-use

13. Market Dynamics

• 13.1. Drivers
• 13.2. Challenges

14. Market Trends and Developments

15. Company Profiles

• 15.1. Amelia US LLC
  • 15.1.1. Business Overview
  • 15.1.2. Key Revenue and Financials
  • 15.1.3. Recent Developments
  • 15.1.4. Key Personnel/Key Contact Person
  • 15.1.5. Key Product/Services Offered
• 15.2. BuildGroup
  • 15.2.1. Business Overview
  • 15.2.2. Key Revenue and Financials
  • 15.2.3. Recent Developments
  • 15.2.4. Key Personnel/Key Contact Person
  • 15.2.5. Key Product/Services Offered
• 15.3. DataRobot, Inc.
  • 15.3.1. Business Overview
  • 15.3.2. Key Revenue and Financials
  • 15.3.3. Recent Developments
  • 15.3.4. Key Personnel/Key Contact Person
  • 15.3.5. Key Product/Services Offered
• 15.4. Ditto.ai
  • 15.4.1. Business Overview
  • 15.4.2. Key Revenue and Financials
  • 15.4.3. Recent Developments
  • 15.4.4. Key Personnel/Key Contact Person
  • 15.4.5. Key Product/Services Offered
• 15.5. DarwinAI
  • 15.5.1. Business Overview
  • 15.5.2. Key Revenue and Financials
  • 15.5.3. Recent Developments
  • 15.5.4. Key Personnel/Key Contact Person
  • 15.5.5. Key Product/Services Offered
• 15.6. Factmata
  • 15.6.1. Business Overview
  • 15.6.2. Key Revenue and Financials
  • 15.6.3. Recent Developments
  • 15.6.4. Key Personnel/Key Contact Person
  • 15.6.5. Key Product/Services Offered
• 15.7. Google LLC
  • 15.7.1. Business Overview
  • 15.7.2. Key Revenue and Financials
  • 15.7.3. Recent Developments
  • 15.7.4. Key Personnel/Key Contact Person
  • 15.7.5. Key Product/Services Offered
• 15.8. IBM Corporation
  • 15.8.1. Business Overview
  • 15.8.2. Key Revenue and Financials
  • 15.8.3. Recent Developments
  • 15.8.4. Key Personnel/Key Contact Person
  • 15.8.5. Key Product/Services Offered
• 15.9. Kyndi
  • 15.9.1. Business Overview
  • 15.9.2. Key Revenue and Financials
  • 15.9.3. Recent Developments
  • 15.9.4. Key Personnel/Key Contact Person
  • 15.9.5. Key Product/Services Offered
• 15.10. Microsoft Corporation
  • 15.10.1. Business Overview
  • 15.10.2. Key Revenue and Financials
  • 15.10.3. Recent Developments
  • 15.10.4. Key Personnel/Key Contact Person
  • 15.10.5. Key Product/Services Offered

16. Strategic Recommendations

17. About Us & Disclaimer