Explainable AI (XAI): Bridge of Trust Between AI and Humans

In this day and age, businesses are increasingly relying on AI to inform business decisions. These decisions are bound to affect individuals’ rights and safety. To ensure that, you must understand how AI algorithms work. This is called explainable AI.

How do AI models reach their conclusions? How do they use their data, and can we trust their results? Explainable AI attempts to answer these questions.

We will talk about how decision-makers in business can employ explainable AI to understand the functioning of an AI model and comprehend how they deliver their insights. We are also going to understand how it is a great mechanism for users to trust the results given by AI.

What is Explainable AI?

Simply put, explainable AI is a way by which human users can comprehend and trust the results and output given by AI models. It can be used to determine the impact and potential biases of an AI model.

Explainable AI helps in characterizing the fairness, accuracy and transparency of a decision by an AI model. Getting the “AI explainability” right is extremely significant as it inspires confidence in the users using it.

How Explainable AI is Used in Decision Making?

The technique used in XAI (Explainable AI) contains three main methods. They are as follows:

Prediction Accuracy

A key component of an AI model’s success is the accuracy with which it gives results. One runs simulations and compares XAI output to training datasets to determine the accuracy.

Traceability

Another significant technique by which explainable AI occurs is traceability. This is achieved by limiting the way decisions are made and by narrowing down the machine learning rules and features.

Decision Understanding

This method is about the human trust factor. For people who are working with AI and don’t trust it, they need to learn to trust it. For such team members, educating them about the AI function is important so that they understand why and how AI makes decisions.

What are the Benefits of Explainable AI?

Operational AI with Trust & Confidence

One of the major benefits of XAI is that it places faith in AI’s workings. It also ensures AI models’ interpretability and explainability. Transparency and traceability are other advantages.

Improve Time to AI Results

Explainable AI makes monitoring and managing AI models to optimize business outcomes possible. It allows for fine-tuning of model development and improves overall model performance.

Mitigating Risks and Lowering the Costs

When you keep your models explainable and transparent, you also help mitigate risks associated with AI model output and lower costs.

The Challenges of Implementing Explainable AI

XAI is about comprehending why an AI model makes a certain decision, prediction, recommendation, or output. To gain this ability, one needs to understand how an AI model operates. Sure, it sounds simple enough, but it’s not.

The more sophisticated the AI system, the more complex its algorithm. Thus, it becomes difficult to pinpoint how exactly a model derived its AI insights. Also, the AI engines tend to get “smarter” by ingesting new data, processing new algorithmic combinations, and updating the output.

It does that with blazing speeds, sometimes delivering results in less than a fraction of a second. Different users of AI system data have different needs. For example, a bank disburses loans, with AI making the credit decisions.

Now, if an applicant is denied a loan, bankers and AI practitioners might need more granular details on why that request was denied. They might also have to confirm that this particular decision is not biased against certain applicants. Regulating and controlling all this is certainly a challenge.

Key Conclusion and Analysis

They say trust is the most precious entity on earth. We feel a scenario in which humans can trust AI is a good place to start. AI’s explainability is the right step in that direction.

At a time when governments all around the world still struggle to understand the consequences of this newly bred piece of technology and somehow try to regulate it, XAI can be a guiding hand.

In this regard, the US administration passed an AI bill of rights aiming to protect personal data and limit surveillance. The Federal Trade Commission also monitors how organizations collect data and use algorithms.

Accountability is the foundation of any good system, and XAI can provide that for AI left, right, and center. Best AI companies in the US ensure they utilize explainable AI tools to their full potential.

BluEnt has made a name for itself in this race. Whether it be AI data analytics or something as immediate as real data analytics, the company’s work speaks volumes. Get in touch with our experts to understand more about our services. We are happy to help.