Explainable AI Market Size

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.
 

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.