Episode 48: Toward an Era Where We Can Ask AI “Why?” — Building Trust Through Explainable AI

When You Want to Ask AI “Why?”

In our daily lives, we encounter AI decisions more often than we might realize. For example, when shopping online, we see recommended products tailored to us, or when using a smartphone camera, it automatically adjusts settings based on the scene. These features are convenient, but have you ever wondered, “Why did it choose this result?”

There’s a growing idea aimed at answering that very question. It’s called Explainable AI. This refers to technologies and systems designed to help people understand the reasons behind an AI’s decision.

What Is Explainable AI? – Shedding Light on Hidden Decisions

Modern AI technologies like machine learning and deep learning analyze vast amounts of data to identify patterns and make decisions. However, these processes are often so complex that it becomes difficult for humans to understand how a particular conclusion was reached.

It’s like being told by a very smart fortune teller that “you’re compatible with this person,” but when you ask why, they won’t explain. Even if the result is positive, not knowing the reason can leave you feeling uneasy.

Explainable AI aims to open up this “black box” nature of AI by making its decision-making process more transparent. For instance, if an image is identified as a cat, the system might explain that it made this judgment based on features like ear shape, eye position, and fur texture—characteristics commonly found in cats.

This isn’t just about being helpful—it plays a vital role in ensuring safety, fairness, and most importantly, trust.

Applications in Healthcare and Finance – How Explanation Builds Trust

Explainable AI is especially important in areas where human lives or financial matters are involved.

Take healthcare as an example. If an AI says “this X-ray shows an abnormality,” but doesn’t explain why, both doctors and patients may feel uneasy. But if it adds that “there’s a denser shadow in the upper right lung area matching previous cases,” then the diagnosis becomes easier to accept and more useful for treatment planning.

The same applies in finance. If an AI denies someone a loan without giving any reason, it may seem unfair. But if it explains that “based on income history and repayment patterns, there’s a high risk,” people are more likely to understand—even if they don’t agree with the outcome.

That said, making things explainable still comes with challenges. Some highly accurate AIs become harder to interpret as their complexity increases—a situation known as a trade-off. Also, what one person finds easy to understand might not be clear to someone else. So there’s no one-size-fits-all explanation method.

Even so, many researchers and companies continue working on Explainable AI because they believe in creating technology people can use with confidence.

Toward a Future Where Humans and AI Understand Each Other

Among humans too, asking questions like “Why do you think that?” often leads to deeper conversations and mutual understanding. In much the same way, we’re moving toward an era where we can ask those same questions of AI: “Why did you come up with that answer?”

Explainable AI is quietly but steadily spreading as the technology that will allow such conversations. It represents not just technical progress but also a new kind of relationship between people and technology.

In our next article, we’ll explore another key pillar supporting trustworthy AI: governance. We’ll look into what kinds of rules or systems are needed to address issues that technology alone can’t solve—and uncover the thinking behind them together.