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Why Explainable AI is Critical

Explainable AI A modern image of a man trapped inside a bubble, unable to know what is happening

What’s Explainable AI?

That’s when AI gives you the details of its every action and decision process it takes. You need that information to understand the what and why of the actions the AI has taken or the decisions it’s made.  By the way, it’s also known as transparency.

Why is Explainable AI important?

The lack of Explainable AI  is a key limitation for today’s Generative AI tools.  They are “black box” systems.  That means they don’t hare the logic on why they make a decision, like selecting specific pieces of data or analyzing and recommending an action.

As you can imagine, government regulations and strict compliance demands require you to explain business processes in detail. That makes Explainable AI critical for AI’s broad deployment.

For example, financial services firms have volumes of regulations. Using AI requires a comprehensive audit trail of every action and decision. Without Explainable AI, financial services organizations are limited with current “black box” AI options.

Explainability also helps us understand what happens in certain situations. For example, if something goes wrong in a software upgrade, you need to know how every step of the upgrade progressed to identify and resolve the issue.

Digital workers deliver Explainable AI by documenting every single action, decision, and the logic behind them.


Why Do You Need Explainable AI?

With all its potential, we all need to be thoughtful about Generative AI.  The known challenges relating to Gneerative AI include:

Sometimes questionable accuracy.

Generative AI is known to sometimes deliver less-than-accurate information, as well as what insiders call “hallucinations,” meaning untrue concepts and content.

This is one reason that digital workers have Humans in the Loop (HIL) to check and refine the resulting outputs. 

Lack of transparency.

Generative AI models don’t provide and audit trail of actions or reasons for making decisions. Since GenAI can be quite unpredictable, that makes Gen AI questionable in regulated industries.

Digital workers solve this problem by providing a comprehensive audit trail, aka Explainable AI.

Learned Bias.

Generative AI learns from the data that’s used to train it. Organizations need to rigorously identify biased responses, addressing and correcting them.

Humans in the Loop enable digital workers to identify bias at its inception. Digital workers can then learn about their bias, and learn to avoid it.

Intellectual property (IP) and copyright questions.

Large Language  Models (LLMs) are the sources for information and training of Generative AI.  They leverage a wide range of publicly available data from the internet.

We’ve already seen the fallout from information that’s been accessed that was, in fact, protected, private or copyrighted. For now need to take the appropriate actions to avoid accidentally sharing IP or using copyrighted information.

Since digital workers audit every single action, application and data involved in their work,  Humans in the Loop can oversee content to avoid these issues. Even better, digital workers can actively watch for, identify and proactively alert their human partners to potential IP or copyright issues. 


Trustworthy and Transparent Digital Workers

As we’ve noted, digital workers capture and document comprehensive audit trails of all actions, applications, data access and processes.  They also document the logic behind any and all decisions, such as a credit denial.

This deep auditing provides the detailed documentation you need for compliance, as well as for internal tracking. Instead of knowledge workers spending days or weeks creating and checking compliance reports, your digital worker provides you detailed information.

Digital workers also act as compliance agents, monitoring actions and data as its used, watching for potential compliance violations and alerting human workers to the potential issues.

Imagine a digital worker that’s collecting the data needed for a due diligence comp book. The information is spread across internal and external sources, including the corporate client’s internal data, your internal data and publicly available internet information.

Your digital worker searches for all available information concerning the target comps. It consolidates this information to create the “book” for analysts to further develop and finalize.

How can the analysts know that the information is valid, available, trustworthy and secure?

They check the digital worker’s audit history. It contains the details they need concerning the source of all  reviewed and collected information,  as well as how that information was consolidated and normalized for analysis.

This information can be used internally for compliance reporting and to assure everyone that the information is accurate and safe to apply.  The same goes for external communication, for example to assure clients that all comps are indeed, accurate and secure.

This is one aspect of digital workers that makes them an excellent choice to deploy artificial intelligence in your enterprise.

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