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可解釋的人工智慧市場 - 全球產業規模、佔有率、趨勢、機會和預測，按組件、部署、按應用、最終用途、地區、競爭細分，2018-2028
市場調查報告書
商品編碼
1379742

可解釋的人工智慧市場 - 全球產業規模、佔有率、趨勢、機會和預測，按組件、部署、按應用、最終用途、地區、競爭細分，2018-2028

Explainable AI Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Component, By Deployment, By Application, By End-use, By Region, By Competition, 2018-2028
出版日期: | 出版商: TechSci Research | 英文 181 Pages | 商品交期: 2-3個工作天內

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簡介目錄

2022 年全球可解釋人工智慧市場價值為 54 億美元，預計在預測期內將強勁成長，到 2028 年複合CAGR為22.4%。隨著組織擴大採用人工智慧，全球可解釋人工智慧(XAI) 市場正在經歷顯著成長跨各產業的解決方案。 XAI是指人工智慧系統為其決策和行動提供可理解和可解釋的解釋的能力，解決傳統人工智慧的「黑盒子」挑戰。在對透明度、問責制和道德人工智慧部署日益成長的需求的推動下，市場即將擴張。 XAI 在金融、醫療保健和自動駕駛汽車等領域至關重要，在這些領域，理解人工智慧產生的決策的能力對於監管合規性和用戶信任至關重要。此外，人工智慧相關法規和指南的興起進一步推動了對 XAI 解決方案的需求。該市場的特點是機器學習技術、演算法和模型架構的創新，這些創新增強了人工智慧系統的可解釋性。隨著企業優先考慮負責任的人工智慧實踐，可解釋的人工智慧市場將繼續其成長軌跡，提供的解決方案不僅能提供人工智慧驅動的見解，還能確保透明度和以人為本的人工智慧決策過程。

主要市場促進因素

決策的透明度

市場概況
預測期 2024-2028
2022 年市場規模 54億美元
2028 年市場規模 183.2億美元
2023-2028 年CAGR 22.4%
成長最快的細分市場 雲
最大的市場 北美洲

由於人工智慧 (AI) 系統對透明度和可解釋性的需求不斷成長，全球可解釋人工智慧 (XAI) 市場正在顯著成長。 XAI 在醫療保健、金融和自動駕駛汽車等各個領域發揮著至關重要的作用，在這些領域，理解人工智慧系統所做的決策對於監管合規性和用戶信任至關重要。隨著人工智慧的日益普及，需要解決人工智慧模型和演算法的複雜性，這使得 XAI 解決方案變得越來越不可或缺。市場的繁榮得益於機器學習技術和演算法的不斷創新，這些技術和演算法增強了人工智慧系統的可解釋性，確保組織能夠利用人工智慧的力量，同時堅持問責制和道德人工智慧實踐。

監理合規性

由於與人工智慧相關的法規和指南數量不斷增加，可解釋人工智慧（XAI）的全球市場正在經歷顯著成長。政府和產業監管機構非常重視人工智慧道德實踐，這迫使組織採用 XAI 解決方案來滿足合規性要求。隨著監管框架的不斷發展，XAI 在幫助組織確保其人工智慧系統遵守法律和道德標準方面發揮著至關重要的作用。在監管要求的推動下，對 XAI 的需求不斷成長，在資料隱私、公平和問責至關重要的行業中尤為突出。全球範圍內人工智慧相關法規和指南的激增，為XAI市場的蓬勃發展創造了有利的環境。政府和監管機構正在認知到與缺乏透明度和可解釋性的人工智慧系統相關的潛在風險。因此，他們正在採取措施，確保負責任地開發和部署人工智慧技術。這些法規通常要求組織對其人工智慧系統所做的決策提供解釋，特別是在醫療保健、金融和刑事司法等關鍵領域。透過採用 XAI 解決方案，組織可以滿足這些監管要求並展示其對道德 AI 實踐的承諾。 XAI 使組織能夠理解和解釋人工智慧生成決策背後的推理，使決策過程更加透明和負責任。這不僅有助於組織遵守法規，還可以培養利害關係人（包括客戶、員工和公眾）之間的信任。

處理敏感資料的行業（例如醫療保健和金融）特別依賴 XAI 來確保資料隱私和公平性。 XAI 技術使組織能夠識別並減輕人工智慧模型中的偏差，確保決策不受種族、性別或社會經濟地位等因素的影響。此外，XAI 使組織能夠檢測並糾正人工智慧系統中的任何意外後果或錯誤，從而最大限度地減少對個人或社會的潛在危害。隨著監管環境的不斷發展，對 XAI 的需求預計將進一步成長。各行業的組織都認知到將其人工智慧系統與法律和道德標準保持一致的重要性。透過採用 XAI，這些組織不僅可以滿足合規性要求，還可以透過展示其對負責任的人工智慧實踐的承諾來獲得競爭優勢。隨著越來越多的產業在人工智慧部署中優先考慮透明度、公平性和問責制，XAI 市場有望大幅擴張。

改進的決策支持

XAI（可解釋人工智慧）是一種強大的工具，可以為人工智慧系統產生的見解提供清晰易懂的解釋，從而使企業和專業人士能夠增強決策過程。事實證明，這項技術在醫療保健和金融等領域特別有價值，它可以幫助臨床醫生、分析師和決策者有效地理解和利用人工智慧驅動的資訊。在醫療保健產業，XAI 在支持臨床醫生理解人工智慧產生的診斷和治療建議方面發揮著至關重要的作用。透過為人工智慧模型產生的見解提供易於理解的解釋，XAI 可以幫助醫療保健專業人員更深入地了解這些建議背後的推理。這反過來又會改善患者護理，因為臨床醫生可以根據人工智慧驅動的見解做出更明智的決策。 XAI 充當人工智慧系統中使用的複雜演算法和人類決策者之間的橋樑，使醫療保健專業人員能夠信任並充分利用人工智慧技術的潛力。同樣，在金融領域，XAI 成為分析師和決策者的寶貴工具。隨著人工智慧驅動的投資策略擴大採用，XAI 有助於理解這些策略背後的推理。透過提供透明且可解釋的解釋，XAI 使金融專業人士能夠清楚地了解人工智慧模型產生的見解。這使他們能夠在投資、風險管理和整體投資組合管理方面做出更明智的決策。 XAI 在金融機構中的使用有助於彌合人工智慧模型的複雜性與人類決策者需要清楚了解其基本原理之間的差距。

由於人們認知到 XAI 作為決策支援工具的價值，XAI 市場正在經歷顯著成長。隨著企業和專業人士越來越認知到對人工智慧產生的見解進行可理解的解釋的重要性，對 XAI 的需求持續成長。 XAI 彌合複雜人工智慧模型和人類決策者之間差距的能力被視為釋放人工智慧技術在各行業的全部潛力的關鍵因素。透過幫助企業和專業人士做出更明智的決策，XAI 正在推動醫療保健和金融等領域的積極變革並改善成果。

增強使用者信任

人工智慧日益融入我們的日常生活，凸顯了建立使用者對人工智慧系統信任的至關重要性。培養這種信任的一種方法是採用可解釋的人工智慧（XAI），其目的是使人工智慧系統透明且可解釋，從而消除與人工智慧「黑盒子」性質相關的擔憂。 XAI 的這一方面對於自動駕駛汽車和關鍵基礎設施等領域尤其重要，因為這些領域的安全性和可靠性至關重要。因此，組織正在認知到 XAI 在增強用戶對人工智慧技術的信心方面的重要性，從而導致市場的顯著擴張。

在人工智慧日益普及的時代，使用者對人工智慧系統內部運作的擔憂是可以理解的。人工智慧傳統的「黑盒子」性質，即在沒有明確解釋的情況下做出決策，引發了人們對這些系統的可靠性、公平性和問責制的質疑。 XAI 透過提供人工智慧系統如何做出決策的見解來解決這些問題，使決策過程對使用者來說更加透明且易於理解。在自動駕駛汽車等領域，人工智慧在確保安全和高效的交通方面發揮著至關重要的作用，用戶的信任至關重要。解釋人工智慧驅動決策背後的推理能力可以幫助減輕與事故或故障相關的擔憂。透過提供清晰的解釋，XAI 使用戶能夠理解為什麼做出特定決定，增強他們對技術的信心並培養信任。

同樣，在能源、醫療保健和金融等關鍵基礎設施領域，依賴人工智慧系統做出重要決策，XAI 在確保這些系統的安全性和可靠性方面可以發揮至關重要的作用。透過讓人工智慧系統變得可解釋，組織可以解決與偏見、錯誤或惡意攻擊相關的問題，從而增強使用者對該技術的信任和信心。有鑑於使用者信任對人工智慧系統的重要性，組織正在投資 XAI 以增強對人工智慧技術的信心。這項投資是因為我們認知到用戶信任是市場擴張的關鍵驅動力。透過採用 XAI，組織可以透過提供透明且可解釋的 AI 系統來脫穎而出，從而吸引更多用戶和客戶。

主要市場挑戰

對可解釋人工智慧的理解有限

全球可解釋人工智慧市場面臨的主要挑戰之一是組織對採用可解釋人工智慧解決方案的重要性和好處的理解和認知有限。許多企業可能沒有完全理解人工智慧模型解釋能力的重要性以及與黑盒演算法相關的潛在風險。這種意識的缺乏可能會導致人們在投資可解釋的人工智慧方面猶豫不決，從而使組織容易受到決策偏見、缺乏透明度和監管合規問題等問題的影響。應對這項挑戰需要全面的教育舉措，以強調可解釋的人工智慧在建立信任、確保公平和實現人工智慧系統的可解釋性方面發揮的關鍵作用。組織需要認知到，可解釋的人工智慧可以提供有關人工智慧模型如何做出決策的見解、增強問責制並促進更好的決策過程。現實世界的例子和案例研究展示了可解釋的人工智慧的實際好處，有助於加深對其重要性的理解。

實施和整合的複雜性

可解釋的人工智慧解決方案的實施和整合可能會給組織帶來複雜的挑戰，特別是那些技術專業知識或資源有限的組織。有效配置和部署可解釋的人工智慧模型，並將其與現有人工智慧系統和工作流程整合，在技術上要求很高。整合過程中可能會出現相容性問題，從而導致延遲和效能不佳。為了應對這些挑戰，簡化可解釋的人工智慧解決方案的部署和管理至關重要。應提供使用者友善的介面和直覺的配置選項，以簡化設定和自訂。此外，組織應該能夠獲得全面的支援和指導，包括文件、教程和技術專家，他們可以協助整合和解決任何問題。簡化可解釋的人工智慧實施的這些方面可以帶來更有效率的流程並提高模型的可解釋性。

平衡解釋能力和績效。

可解釋的人工智慧模型旨在提供透明度和可解釋性，但它們面臨著在解釋能力和性能之間取得適當平衡的挑戰。高度可解釋的模型可能會犧牲預測準確性，而複雜的模型可能缺乏可解釋性。組織需要在模型解釋能力和效能之間找到最佳權衡，以確保人工智慧系統既可信又有效。這項挑戰需要持續的研究和開發工作，以提高人工智慧模型的可解釋性，同時又不影響其效能。先進的技術，例如與模型無關的方法和事後可解釋性方法，可以透過提供對模型行為和決策過程的見解來幫助應對這項挑戰。努力在這些領域不斷改進將使組織能夠有效利用可解釋的人工智慧，同時保持高效能標準。

監管和道德考慮

全球可解釋人工智慧市場也面臨著監管合規和道德考量的挑戰。隨著人工智慧系統在醫療保健、金融和自動駕駛汽車等關鍵領域變得越來越普遍，對透明度和問責制的需求也越來越大。監管框架正在製定中，以確保人工智慧系統公平、公正且可解釋。組織必須應對這些不斷變化的法規，並確保其可解釋的人工智慧解決方案符合法律和道德標準。這項挑戰要求組織隨時了解最新的監管動態，並投資於強大的治理框架，以解決潛在的偏見、歧視和隱私問題。行業利益相關者、政策制定者和研究人員之間的合作對於制定促進負責任和合乎道德地使用可解釋人工智慧的指南和標準至關重要。

主要市場趨勢

對可解釋的人工智慧解決方案的需求增加

隨著組織認知到人工智慧系統透明度和可解釋性的重要性，可解釋人工智慧 (XAI) 的全球市場需求激增。隨著人工智慧在各行業的日益普及，人們越來越需要了解人工智慧演算法如何做出決策並為其輸出提供解釋。這項需求是由監管要求、道德考慮以及與最終用戶建立信任的需要所驅動的。

可解釋的人工智慧解決方案旨在透過提供對人工智慧模型決策過程的洞察來解決「黑盒子」問題。這些解決方案利用基於規則的系統、與模型無關的方法和可解釋的機器學習演算法等技術來產生人類易於理解的解釋。透過提供清晰的解釋，組織可以深入了解影響人工智慧決策的因素，識別潛在的偏見，並確保人工智慧系統的公平性和問責制。

轉向針對特定產業的可解釋人工智慧解決方案

全球市場正在經歷向特定行業的可解釋人工智慧解決方案的轉變。由於不同行業有獨特的需求和挑戰，因此需要客製化的 XAI 解決方案來有效解決特定用例。組織正在尋求能夠提供與其行業領域（例如醫療保健、金融或製造）相關的解釋的 XAI 解決方案。

行業特定的 XAI 解決方案利用領域知識和上下文資訊來產生對最終用戶有意義且可操作的解釋。這些解決方案使組織能夠更深入地了解其特定行業背景下的人工智慧決策流程，從而提高信任、做出更好的決策並增強監管合規性。

人機協作一體化

人機協作的融合是全球可解釋人工智慧市場的重要趨勢。 XAI 解決方案的目的不是取代人類，而是透過提供可解釋的見解和解釋來增強人類決策。人類和人工智慧系統之間的這種協作使用戶能夠理解人工智慧輸出背後的推理，並根據這些解釋做出明智的決策。

可解釋的人工智慧解決方案透過使用視覺化、自然語言解釋或互動式介面以使用者友好的方式呈現解釋，促進人類與人工智慧的協作。這允許用戶與人工智慧系統互動、提出問題並探索不同的場景，以更深入地了解人工智慧生成的輸出。透過促進協作，組織可以利用人類和人工智慧系統的優勢，從而實現更可靠、更值得信賴的決策過程。

細分市場洞察

最終用途見解

根據最終用途，市場分為醫療保健、BFSI、航空航太和國防、零售和電子商務、公共部門和公用事業、IT 和電信、汽車等。 2022 年，IT 和電信業的收入佔比最高，達到 17.99%。5G 和物聯網 (IoT) 的推出使組織和個人能夠即時收集更多真實世界的資料。人工智慧 (AI) 系統可以利用這些資料變得越來越複雜和強大。

借助電信領域的人工智慧，行動營運商可以增強連接性和客戶體驗。行動電信商可以利用人工智慧最佳化和自動化網路，提供更好的服務，讓更多人能夠連接。例如，AT&T 透過利用人工智慧和統計演算法的預測模型來預測和防止網路服務中斷，而 Telenor 使用先進的資料分析來降低其無線電網路中的能源使用和二氧化碳排放。人工智慧系統還可以支援與客戶進行更個人化和有意義的互動。

BFSI 中的可解釋人工智慧預計將透過提高生產力和降低成本，同時提高向客戶提供的服務和商品的品質，為金融組織帶來競爭優勢。這些競爭優勢隨後可以透過提供更高品質和更個人化的產品、發布資料洞察來指導投資策略以及透過對信用記錄很少的客戶進行信用度分析來增強金融普惠性而使金融消費者受益。預計這些因素將促進市場成長。

部署見解

根據部署，市場分為雲端和本地。 2022 年，本地細分市場佔據最大的收入佔有率，為 55.73%。使用本地可解釋的 AI 可以帶來多種好處，例如提高資料安全性、減少延遲以及增強對 AI 系統的控制。此外，對於受到限制使用基於雲端的服務的監管要求的組織來說可能更可取。組織使用基於規則的系統、決策樹和基於模型的解釋等各種技術來實現本地可解釋的人工智慧。這些技術可以深入了解人工智慧系統如何做出特定決策或預測，使用戶能夠驗證系統的推理並識別潛在的偏差或錯誤。

各個垂直行業的主要參與者，尤其是 BFSI、零售業和政府，更喜歡在本地部署 XAI，因為它具有安全優勢。例如，金融服務公司摩根大通在本地使用可解釋的人工智慧來改善詐欺偵測並防止洗錢。該系統使用機器學習來分析大量資料，識別潛在的詐欺活動，並為其決策提供清晰透明的解釋。同樣，科技公司 IBM 提供了一個名為 Watson OpenScale 的本地可解釋人工智慧平台，該平台可協助組織管理和監控其人工智慧系統的效能和透明度。該平台為人工智慧決策和預測提供了清晰的解釋，並允許組織追蹤和分析用於訓練其人工智慧模型的資料。

應用洞察

根據應用，市場分為詐欺和異常檢測、藥物發現和診斷、預測性維護、供應鏈管理、身分和存取管理等。人工智慧 (AI) 在詐欺管理中發揮著至關重要的作用。 2022 年，在詐欺和異常檢測領域佔據最大收入佔有率，達到 23.86%。

機器學習 (ML) 演算法是人工智慧的一個組成部分，可以檢查大量資料，以識別可能表明詐欺活動的趨勢和異常情況。由人工智慧驅動的詐欺管理系統可以偵測並阻止各種欺詐，包括金融詐欺、身分盜竊和網路釣魚嘗試。他們還可以改變並發現新的詐欺模式和趨勢，從而提高檢測能力。

XAI 在具有預測性維護的製造中的突出應用正在推動市場成長。製造業中的 XAI 預測分析涉及使用可解釋的 AI 模型對製造業進行預測並產生見解。可解釋的人工智慧技術用於開發預測製造工廠設備故障或維護需求的模型。透過分析歷史感測器資料、維護日誌和其他相關資訊，XAI 模型可以識別導致設備故障的關鍵因素，並為預測的維護需求提供可解釋的解釋。

此外，可解釋的人工智慧模型在品質控制過程中利用預測分析。透過分析生產資料、感測器讀數和其他相關參數，XAI 模型可以預測製造過程中出現缺陷或偏差的可能性。這些模型還可以解釋導致品質問題的因素，幫助製造商了解根本原因並採取糾正措施。

區域洞察

北美在 2022 年佔據市場主導地位，佔有率為 40.52%，預計在預測期內CAGR為 13.4%。德國、法國、美國、英國、日本和加拿大等已開發國家強大的IT基礎設施是支持這些國家可解釋人工智慧市場成長的主要因素。

推動這些國家可解釋人工智慧市場擴張的另一個因素是這些國家政府對更新IT基礎設施的大力援助。然而，印度和中國等發展中國家預計在預測期內將出現更高的成長。這些國家良好的經濟成長吸引了許多適合擴展可解釋人工智慧業務的投資。

預計亞太地區在預測期內將以 24.8% 的最快CAGR成長。亞太國家技術的顯著進步正在推動市場成長。例如，2021 年 2 月，日本富士通實驗室和北海道大學開發了一種基於「可解釋的人工智慧」原則的新系統。它會根據有關資料（例如來自醫療檢查的數據）的人工智慧結果，自動向用戶顯示獲得所需結果所需執行的步驟。

目錄

第 1 章：產品概述

  • 市場定義
  • 市場範圍
    • 涵蓋的市場
    • 研究年份
    • 主要市場區隔

第 2 章：研究方法

  • 研究目的
  • 基線方法
  • 範圍的製定
  • 假設和限制
  • 研究來源
    • 二次研究
    • 初步研究
  • 市場研究方法
    • 自下而上的方法
    • 自上而下的方法
  • 計算市場規模和市場佔有率所遵循的方法
  • 預測方法
    • 數據三角測量與驗證

第 3 章：執行摘要

第 4 章：COVID-19 對全球可解釋人工智慧市場的影響

第 5 章：客戶之聲

第 6 章：全球可解釋人工智慧市場概述

第 7 章：全球可解釋人工智慧市場展望

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按組件（解決方案、服務）
    • 按部署（雲端、本機）
    • 按應用（詐欺和異常檢測、藥物發現和診斷、預測性維護、供應鏈管理、身分和存取管理、其他）
    • 按最終用途（醫療保健、BFSI、航太和國防、零售和電子商務、公共部門和公用事業、IT 和電信、汽車、其他）
    • 按地區（北美、歐洲、南美、中東和非洲、亞太地區）
  • 按公司分類 (2022)
  • 市場地圖

第 8 章：北美可解釋的人工智慧市場前景

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按組件
    • 按部署
    • 按應用
    • 按最終用途
    • 按國家/地區
  • 北美：國家分析
    • 美國
    • 加拿大
    • 墨西哥

第 9 章：歐洲可解釋的人工智慧市場前景

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按組件
    • 按部署
    • 按應用
    • 按最終用途
    • 按國家/地區
  • 歐洲：國家分析
    • 德國
    • 法國
    • 英國
    • 義大利
    • 西班牙
    • 比利時

第 10 章：南美洲可解釋的人工智慧市場前景

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按組件
    • 按部署
    • 按應用
    • 按最終用途
    • 按國家/地區
  • 南美洲：國家分析
    • 巴西
    • 哥倫比亞
    • 阿根廷
    • 智利
    • 秘魯

第 11 章：中東和非洲可解釋的人工智慧市場前景

  • 市場規模及預測
    • 按價值
  • 市佔率及預測
    • 按組件
    • 按部署
    • 按應用
    • 按最終用途
    • 按國家/地區
  • 中東和非洲：國家分析
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 南非
    • 土耳其
    • 以色列

第 12 章：亞太地區可解釋的人工智慧市場展望

  • 市場規模及預測
    • 按組件
    • 按部署
    • 按應用
    • 按最終用途
    • 按國家/地區
  • 亞太地區：國家分析
    • 中國可解釋的人工智慧
    • 印度可解釋的人工智慧
    • 日本可解釋的人工智慧
    • 韓國可解釋的人工智慧
    • 澳洲可解釋的人工智慧
    • 印尼可解釋的人工智慧
    • 越南可解釋的人工智慧

第 13 章：市場動態

  • 促進要素
  • 挑戰

第 14 章：市場趨勢與發展

第 15 章：公司簡介

  • 阿米莉亞美國有限責任公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel/Key Contact Person
    • Key Product/Services Offered
  • 建構組
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel/Key Contact Person
    • Key Product/Services Offered
  • 數據機器人公司
    • 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
  • 達爾文人工智慧
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel/Key Contact Person
    • Key Product/Services Offered
  • 事實資料
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel/Key Contact Person
    • Key Product/Services Offered
  • 谷歌有限責任公司
    • 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
  • 微軟公司
    • Business Overview
    • Key Revenue and Financials
    • Recent Developments
    • Key Personnel/Key Contact Person
    • Key Product/Services Offered

第 16 章：策略建議

第 17 章：關於我們與免責聲明

簡介目錄
Product Code: 17009

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 Period2024-2028
Market Size 2022USD 5.4 Billion
Market Size 2028USD 18.32 billion
CAGR 2023-202822.4%
Fastest Growing SegmentCloud
Largest MarketNorth 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