Explainable AI Market Size - By Component (Solution, Service), By Software Service (Standalone Software, Integrated Software, Automated Reporting Tools, Interactive Model Visualization), By Method, By Industry Vertical & Forecast, 2024 - 2032

Explainable AI Market Size - By Component (Solution, Service), By Software Service (Standalone Software, Integrated Software, Automated Reporting Tools, Interactive Model Visualization), By Method, By Industry Vertical & Forecast, 2024 - 2032

Explainable AI Market Size

Explainable AI Market size was valued at USD 6.55 billion in 2023 and is expected to grow at a CAGR of over 15% between 2024 and 2032. The market for explainable AI is expected to develop significantly, partly due to ethical and regulatory considerations. Globally, governments and regulatory agencies are becoming more aware of the possible risks that AI systems may pose including bias, discrimination, and a lack of accountability. They are putting into place laws that require AI models to be transparent and explainable to alleviate these risks.

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For example, the General Data Protection Regulation (GDPR) of the European Union contains rules for the right to explanation, which mandates that corporations give explicit justifications for any automated decisions that have an impact on individuals. Similarly, explainable AI is emphasized by the proposed EU Artificial Intelligence Act, especially in high-risk fields such as public administration, banking, and healthcare. The need for explainable AI solutions is fueled by these regulatory frameworks, which companies must abide by to avoid fines and preserve public trust.

Another important factor driving the explainable AI market's growth is improving model performance and debugging. Explainable AI helps data scientists and developers better understand the inner mechanisms of their models by shedding light on the decision-making processes of AI algorithms. This transparency is crucial for locating and fixing biases, mistakes, and other problems that can impair the performance of the model. Developers can enhance the precision, dependability, and equity of their models by comprehending the decision-making process.

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Explainable AI methods make it possible to identify inadvertent biases in algorithms and data, which allows for the implementation of corrective measures to ensure more equitable results. Furthermore, explainable AI facilitates debugging by identifying model components that might be producing unexpected or inaccurate results. This capacity shortens the development period owing to its quicker & more efficient problem-solving abilities.

For instance, in June 2023, IBM unveiled a new platform called IBM Watsonx to it improve organizational operations through AI solutions. The objective of this platform is to enable businesses to efficiently accelerate their operations by utilizing AI technologies.

The difficulty and trade-offs involved in making AI models interpretable are among the major obstacles the explainable AI business encounters. Deep learning models, with their complex structures and large amounts of parameters, frequently function as black boxes in advanced AI. These intricate models are typically necessary to reach high-performance and accuracy levels, but it can be difficult to make them comprehensible.

Simplifying models to increase explainability may reduce their performance, resulting in a trade-off between accuracy and transparency. This trade-off must be balanced using complex approaches and procedures, which can be both resourceful and technically intensive. Furthermore, it is challenging to create a system that works for all stakeholders as different groups, including developers, regulators, and end users, have varied requirements for explainability.

Explainable AI Market Trends

One significant trend propelling the market forward is the use of explainable AI in fundamental business processes. Businesses across a range of sectors are acknowledging the importance of AI transparency to win over stakeholders and customers. Businesses can offer comprehensible insights into their decision-making processes by integrating explainable AI into their operations.

Explainable AI is utilized; for instance, in financial services to support credit decisions and identify fraudulent activity, and in healthcare to clarify recommended diagnoses & treatments. This trend ensures regulatory compliance, while also improving client satisfaction and confidence. Consequently, to improve company operations and preserve competitive advantage, an increasing number of enterprises are prioritizing the use of explainable AI.

The explainable AI market is expanding due to notable developments in explainability methodologies. To provide more advanced and practical techniques for deciphering intricate AI models, researchers and developers are continually exploring new ideas. Strategies such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and attention mechanisms are being improved upon and used more frequently.

Users will find it easier to comprehend and trust AI systems owing to these developments, which allow for more accurate & transparent explanations of its decision-making processes. The acceptance of explainable AI solutions is further fueled by the advancement of model-agnostic interpretability techniques, which enable wider applicability across a variety of AI model types.

Explainable AI is becoming increasingly popular in highly regulated sectors such as insurance, healthcare, and finance. These industries must ensure that their AI systems are accountable and transparent to comply with strict regulations. Explainable AI offers automated judgments with comprehensible explanations, helping to satisfy regulatory requirements. Explainable AI, for instance, is essential in the financial industry to guarantee that credit scoring algorithms do not unintentionally bias against specific populations. It aids medical professionals to comprehend and trust AI-generated diagnosis and therapy recommendations. Explainable AI solutions are anticipated to experience increasing demand in these areas as regulatory scrutiny increases.

Explainable AI Market Analysis

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Based on software type, the market is divided into model-agnostic methods and model-specific methods. The model-agnostic methods segment is expected to register a CAGR of 19.1% during the forecast period.

• Model-agnostic approaches provide a flexible and adaptable way to evaluate and comprehend the outputs of different AI models, making them an essential tool in the explainable AI industry. In contrast to model-specific approaches, which are designed for specific kinds of algorithms (such as neural networks/decision trees), model-agnostic approaches are applicable to any AI model, regardless of its architecture.
• Their considerable value in a variety of application contexts is rooted in their universality. LIME and SHAP are two well-known model-agnostic techniques. To create interpretable models that locally resemble the behavior of the black-box model, LIME first perturbs the input data and then monitors changes in the output.
• Conversely, SHAP provides a unified measure of feature relevance by using ideas from cooperative game theory to assign the model's output to its input characteristics. These techniques allow users to get insights into the decision-making processes of complicated models, discover biases, and successfully evaluate model outputs.
• They are especially helpful for businesses that require transparency and accountability across a range of AI applications. Model-agnostic approaches are becoming increasingly popular in the explainable AI market owing to their adaptability and wide range of applications, which meet the demands of different businesses, seeking trustworthy and comprehensible AI solutions.

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Based on component, the explainable AI market is divided into solution & service. The solution segment dominated the global market with a revenue of over USD 4 billion in 2023.

• The market for explainable AI includes a solution segment that consists of a broad range of goods and services intended to improve the accountability, interpretability, and transparency of AI models. Software tools, platforms, and frameworks that offer features for model interpretation, bias detection, and compliance reporting are included in this category.
• Prominent technology firms and fledgling startups provide all-inclusive explainable artificial intelligence solutions that smoothly mesh with current AI processes and frameworks. For example, explainability features are implemented into systems such as Google Cloud AI, IBM Watson, and Microsoft Azure Machine Learning, which assist developers and data scientists in comprehending & interpreting the predictions of their models.
   
• The solution segments also include professional and consulting services that help firms create best practices for the ethical deployment of AI, ensure regulatory compliance, and adopt & optimize explainable AI techniques.
• The solution market is growing, delivering more advanced and user-friendly solutions that meet the needs of many industries, from banking & healthcare to legal & retail, as the demand for transparency and accountability in AI continues to rise. The creation and acceptance of these solutions are essential for encouraging responsible use of AI technologies while fostering public confidence.

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North America dominated the global explainable AI market in 2023, accounting for a share of over 85%. The market for explainable AI is dominated by the North American region due to a mix of technological advancements, legal frameworks, and large investments in AI R&D. Due to its leadership in technology and AI, the U.S. is an important player.

Prominent technological corporations, such as Google, Microsoft, IBM, and Amazon, have their headquarters located in North America and are leading the way in the development and implementation of explainable AI technology. These businesses make significant investments in R&D to provide innovative AI solutions that put accountability and transparency first.

Furthermore, in response to the ethical and societal implications of AI, North America's regulatory environment is changing. Legislators and regulatory organizations are paying more attention to making sure AI systems are just, open, and responsible. The demand for explainable AI solutions is driven by initiatives such as the U.S. Algorithmic Accountability Act, which highlights the necessity for enterprises to provide explanations for automated decisions.

The U.S. leads the world in explainable AI market owing to its strong technological base, large investments in AI R&D, and forward-thinking legislative framework. The nation is home to significant digital giants that are leading the way in the development of explainable AI, such as Google, Microsoft, IBM, and Amazon. To improve AI transparency and interpretability, these organizations employ specialized teams and heavily invest in AI research.

Explainable AI solutions are also becoming more popular due to the U.S. government's and regulatory agencies' growing emphasis on AI ethics and accountability, including the Federal Trade Commission (FTC). Prominent academic establishments, such as Carnegie Mellon, Stanford, and MIT, make substantial contributions to the field of AI explainability research, encouraging scholarly cooperation and innovations.

With a strong emphasis on technology & innovations, government support, and ethical AI practices, Japan is leading the way in the explainable AI business and growing quickly. Along with financial programs and strategic alliances between the public and commercial sectors, the Japanese government has started several initiatives to support AI research and development. Large Japanese companies, including Fujitsu, Hitachi, and NEC, are actively working on explainable AI solutions to improve AI applications' transparency and a sense of confidence.

Government-established frameworks and rules that stress the value of responsibility and explainability in AI systems are indicative of Japan's approach to AI ethics and governance. Moreover, explainable AI has a lot of potential to enhance decision-making processes in Japan owing to the country's aging population and the problems in healthcare and robotics that come with it.

For instance, in February 2024, Japan is addressing the challenges of a declining workforce brought on by an aging population by providing new opportunities in digital technology and utilizing cutting-edge AI techniques. This offers international businesses the chance to collaborate with domestic partners in this new industrial revolution to help change Japanese society.

Due to its strong technological foundation, proactive government policies, and vibrant AI ecosystem, South Korea is starting to emerge as a major participant in the explainable AI market. The development of AI has been given top priority by the South Korean government as part of its national policy, which includes significant investments in R&D and the encouragement of cooperation between the public and private sectors. Prominent South Korean IT firms, such as Samsung, LG, and Naver are leading the way in the development of AI technologies, such as explainable AI, to guarantee transparency and reliability in their apps.

With endeavors to set rules and standards for AI transparency and accountability, South Korea's regulatory framework is also changing to address ethical problems related to AI. The nation's emphasis on healthcare, driverless vehicles, and smart cities offers substantial prospects for the application of explainable AI, enhancing decision-making processes and ensuring public trust in AI-driven systems.

Due to its significant investments in AI research and development, government backing, and the quick uptake of AI technologies across a wide range of industries, China is a dominant player in the explainable AI market. AI is now a top priority for the Chinese government, which has funded and developed ambitious plans to position China as a leader in AI innovation worldwide.

To maintain transparency and compliance with changing rules, major Chinese IT giants such as Baidu, Alibaba, Tencent, and Huawei are making significant investments in explainable AI research and applications. China has established rules and policies that highlight the significance of explainability and responsibility in AI systems, reflecting its approach to AI ethics and governance. China is seeing a rapid digital transition, especially in industries such as finance, healthcare, and smart cities, which is driving the demand.

Explainable AI Market Share

Microsoft Corporation and International Business Machines Corporation (IBM) held a significant share of over 10% in the explainable AI industry. Microsoft Corporation has a substantial market share in explainable AI due to its substantial investments in AI R&D, strong cloud infrastructure, and a wide range of AI platform offerings. Explainability elements are integrated into a range of AI tools and services offered by the corporation through its cloud computing service, Microsoft Azure.

Developers can comprehend, troubleshoot, and have confidence in their machine learning models with the aid of integrated interpretability tools offered by Azure Machine Learning. Microsoft's AI policies and efforts, such the AI for Good program that stresses responsible AI development, demonstrate the company's dedication to ethical AI and openness. Microsoft Research, the company's research division, constantly advances the field of explainable AI through innovative projects and partnerships with educational institutions.

Due to its extensive product range, ethical AI focus, and long history of AI innovations, International Business Machines Corporation (IBM) has a significant market share in explainable AI. The company's primary AI platform, IBM Watson, has sophisticated explainability features that assist people in comprehending and interpreting insights produced by AI. Watson's Explainability offering promotes confidence by enabling organizations to observe the decision-making process of AI models.

IBM has demonstrated its commitment to ethical AI with the establishment of the AI Ethics Board and the AI Fairness 360 toolbox, which offers resources for identifying and reducing bias in AI models. Explainable AI approaches and technologies are constantly evolving due to IBM's broad research capabilities, which are exemplified by IBM Research.

Explainable AI Market Companies

Major players operating in the explainable AI industry are

• Microsoft Corporation           
• International Business Machines Corporation (IBM)
• Google LLC
• NVIDIA Corporation
• Amazon Web Services, Inc. (AWS)
• Salesforce, Inc.
• DataRobot, Inc.

Explainable AI Industry News

• In June 2023, a 2.6 USD million fundraising round was secured by a Dutch business that specializes in the implementation of machine learning models. The purpose of this investment was to increase the platform's capacity for explainability and transparency while complying with upcoming European AI legislation.
• In March 2023, GyanAI announced the release of the world's first language model and natural language comprehension engine with explainable AI capabilities. This innovation marks a significant milestone in enhancing the accessibility and understanding of AI technologies.

The explainable AI market research report includes in-depth coverage of the industry with estimates & forecasts in terms of revenue (USD billion) from 2021 to 2032, for the following segments

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Market, By Component

• Solution
• Service

Market, By Software Type

• Standalone software
• Integrated software
• Automated reporting tools
• Interactive model visualization

Market, By Method

• Model-agnostic methods
• Model-specific methods

Market, By Component

• BFSI
• Retail & e-commerce
• IT & telecommunication
• Government & public sector
• Healthcare
• Manufacturing
• Media & entertainment
• Others

The above information is provided for the following regions and countries

• North America
  • U.S.
  • Canada
• Europe
  • Germany
  • UK
  • France
  • Italy
  • Spain
  • Rest of Europe
• Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • ANZ
  • Rest of Asia Pacific
• Latin America
  • Brazil
  • Mexico
  • Rest of Latin America
• MEA
  • UAE
  • Saudi Arabia
  • South Africa
  • Rest of MEA