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A Guide to Causal AI

March 30, 2022 No Comments

Featured article by Russell Emmental, Independent Technology Author

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If you still think that a world run on artificial intelligence (AI) is a futuristic phenomenon, think again. The technology is here, and it could be running more systems that touch your daily life than you imagine.

The good news is that artificial intelligence is growing and developing to become more valuable to us every day, and one of these developments is causal AI. But what makes this form different, and could your business benefit from it? Check out this quick guide to causal AI, and learn more about where the world is going.

What Is Causal AI?

Causal AI uses causal inference to reason and predict the way humans do, but more objectively. It considers all the factors at play in a problem, sees how they would affect one another, and determines the likeliest outcome.

Why Causal AI May Be Superior

With other forms of artificial intelligence, the systems run on correlation. However, as the saying goes, correlation doesn’t equal causation.

For example, this study pointed out how one non-causal artificial intelligence system falsely predicted how much care patients would need based on how much they spent on healthcare. The technology completely undermined other important factors, including how there’s less healthcare spending on black Americans due to inequities.

Rather than merely seeing which figures correlate, causal AI can consider all the information available to it and see beyond basic figures to make decisions that’s far closer to human reasoning.

What Industries Will Benefit from Causal AI?

You’ve now gotten a lot of information about the concept of Causal AI. But what can this technology do in practice?

Making predictions that are more reliable than ever seems appealing to just about anyone, but some industries are even more likely to benefit from the power of predicting causality. Here are a few where you’ll likely see more of the technology in the future.

– Asset Management: Strong causal AI gives investors a distinct competitive edge as it considers a vast array of factors beyond simple correlation to understand the market better.

– Energy: The energy sector is changing rapidly due to changing public sentiments, innovations in the industry, and shifting sources of commodities. This technology can help leaders stay on top of all these variables.

– Telecommunications: In an evolving digital age, telecommunications leaders must know where their investments will work best, from purchasing towers to staying on top of their competitors.

– Insurance: With all the fraud in the insurance industry, causal AI can best identify fraudulent claims and protect the assets of insurance companies while improving the customer experience to retain more policyholders.

– Healthcare: From predicting admission surges to preventing high numbers of readmissions, understanding patient behavior can help rebuild the trust between people and their healthcare providers.

– Transportation of Goods: We’ve seen the problems a disrupted supply chain can cause. Causal AI is far better at predicting disruptions, thus allowing companies to stay ahead of problems and supporting the ever-growing demand of the e-commerce industry.

The Slow Adaption of Causal AI

With so many benefits, why aren’t we seeing this technology everywhere yet? The biggest impediment is that we don’t always notice the benefits.

Causal AI can’t always reveal just how certain its likely outcome is. Is the cause 90% reliable or 15%? With similar methods of causal studies, such as the Bayesian Network, they are typically used as proof-of-concept techniques to support standing beliefs rather than discover something new.

Because the system is limited to the information it has, humans may not feel comfortable relying on the outcomes produced as they’re not sure how confident the algorithm would be if it suddenly received new information.

The Future of Causal AI

Artificial intelligence seems to be the way of the future, and strong developments like causal AI systems are what will keep it effective and relevant. However, it will take some time to get these superior systems up and running due to a lack of trust. But for those who could use stronger predictive reasoning in their life or business, causal AI could serve as the perfection solution.

APPLICATION INTEGRATION, CLOUD DATA, DATA and ANALYTICS , DATA SECURITY, MOBILE DATA, SOCIAL BUSINESS

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