Why the Explainable AI Market is Growing Fast

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Thanks to digital transformation, there seems to be no ceiling to the heights companies will reach in the next few years. Artificial intelligence (AI) is one of the most remarkable technologies that are helping companies reach these new heights. But as AI progresses with many use cases, the lingering problem of trust arises: AI is still not fully trusted by humans. At best, it's under intense scrutiny, and we're still a long way from the human-AI synergy that data science and AI experts dream of.

One of the factors underlying this inconsistent reality is the complexity of AI. The other is the opaque approach that AI-driven projects often take to solving problems and making decisions. To solve this problem, several business leaders looking to build trust in AI have turned to Explainable AI models (also known as XAI).

Explainable AI empowers IT managers, especially data scientists and ML engineers, to interrogate, understand, and characterize the accuracy of models and provide transparency in AI-based decision making. AI.

Why companies are jumping on the explainable AI bandwagon

With the size of the global Explainable AI market expected to grow from $3.5 billion in 2020 to $21 billion by 2030, according to a report by ResearchandMarkets, it is evident that more and more companies are now on the explainable AI bandwagon. Alon Lev, CEO of Israel-based Qwak, a fully managed platform that unifies machine learning (ML) engineering and data operations, told VentureBeat in an interview that this trend "may be directly linked to new regulations that require specific industries to provide more transparency about model predictions. The growth of Explainable AI is driven by the need to build trust in AI models, he said. /p> Event

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He further noted that another growing trend in explainable AI is the use of SHAP (SHapley Additive exPlanations) values, which is a game-theoretic approach to explaining the outcome of ML models.

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"We find that our fintech and healthcare clients are more engaged with the topic as they are sometimes required by regulation to explain why a model gave a specific prediction, how the prediction turned out produced and what factors were taken into account. In these specific industries, we are seeing more models with explainable AI built in by default,” he added.

A growing market with difficult problems to solve

There is no shortage of AI and MLops startups, with a long list of startups developing MLops solutions, including Comet, Iterative.ai, ZenML, Landing AI, Domino Data Lab, Weights and Biases and many others. Qwak is another startup in the space that focuses on automating MLops processes and enabling com...