Episode 47: Can AI Be Unfair? Understanding “Fairness” in Artificial Intelligence Decisions

Does AI Have Bias? Understanding the Idea of Fairness

Imagine you’re applying for a job. Even though your resume and interview are the same as someone else’s, you’re evaluated differently just because of your name or where you’re from. That would feel unfair, wouldn’t it?

Surprisingly, similar things can happen with artificial intelligence (AI). We often expect AI to be more rational and impartial than humans. But in reality, AI can also develop “biases.”

The idea of reducing these biases and aiming for fair decisions is captured by the term “fairness.”

Why Fairness Matters in AI

Fairness, simply put, means ensuring that AI’s decisions don’t unfairly disadvantage certain people or groups.

AI becomes smarter by learning from large amounts of data. But if that data contains biases, the AI will learn those biased patterns too. For example, if past hiring data favored male candidates, an AI trained on that data might conclude that men are more likely to be hired. As a result, female applicants could be unfairly treated.

To prevent this kind of issue, developers start by reviewing the data used for training. They also check whether the outcomes are balanced. The word “bias” often comes up here—it refers to those very imbalances in perspective or judgment. So fairness can be seen as the overall effort to reduce such bias as much as possible.

While some technical methods may be complex, what matters most is the intention to create decisions that people can agree are reasonable.

Everyday Examples of Fairness: From Music to Services

Let’s look at a more everyday example. Music streaming services use AI to recommend songs based on your preferences. But what if those recommendations were based only on your age or gender? If you’re young and always get pop music suggestions—or older and only get enka—you might end up hearing songs that don’t match your actual taste.

Ideally, recommendations should come from your listening history and interests—not assumptions about who you are. In this way, fairness isn’t just about treating everyone identically; it’s also about respecting each person’s individuality.

That said, fairness isn’t always simple. What feels fair can vary depending on personal values or perspectives. And if we focus too much on fairness alone, we might end up lowering accuracy or efficiency in some systems.

This is why fairness requires not only technical solutions but also ethical thinking—asking questions like “Is this really right from a human standpoint?” In recent years, efforts have been growing to combine fairness with ideas like “Explainable AI,” which aims to make it clear why an AI made a certain decision.

Toward a More Compassionate AI: Challenges and Future Outlook

Today, AI plays a major role in our daily lives. That makes it even more important to think about how decisions are made—and whether anyone might be unintentionally harmed in the process.

The word “fairness” carries not just technical meaning but also a sense of care for others. Perfect fairness may not exist—but striving toward it still matters deeply. That attitude may be what helps build better relationships between people and AI.

In our next article, we’ll explore how we can ask AI: “Why did you make that decision?” We’ll take a closer look at Explainable AI—a new approach for building trust with intelligent systems.