When Do People Surrender Their Decisions to Black Box Algorithms?

Artificial intelligence systems have moved beyond merely suggesting films or displaying advertisements. Today, many critical decisions—from credit applications to medical diagnoses, hiring processes to autonomous vehicles—are made or guided by AI-supported systems. However, this advancement brings forward a crucial question: How much trust can people place in algorithms whose workings they do not understand?

At the heart of this question lies the “Black Box,” that is, opaque algorithms. Particularly with deep learning models that operate with millions or even billions of parameters, it is often impossible to explain how they arrive at a given conclusion. Even when the outcome is correct, the lack of understanding about how the decision was generated creates a trust problem. This is precisely where the concept of Explainable Artificial Intelligence (Explainable AI - XAI) becomes a critical necessity.

When a human expert makes a decision, they can typically explain their reasoning. A doctor can justify why they made a particular diagnosis; an engineer can explain why they chose a specific design. However, many AI models produce only outcomes without revealing the logic behind them. For example, suppose a bank uses an AI system that rejects a credit application. When the customer asks, “Why was I rejected?”, the system responds only with “The algorithm did not find it suitable.” This response creates serious legal and ethical issues. It not only undermines user trust but also makes it harder to detect potential errors and biases within the system. ^【1】

Human trust in technology is not always proportional to its performance. No matter how successful a system is, people begin to doubt it once they cannot comprehend its decision-making mechanism.

Psychological research shows that humans have a strong tendency to avoid uncertainty. Knowing why a decision was made increases feelings of trust, regardless of whether the decision is correct. As a result, people sometimes prefer systems with lower performance but greater transparency over more accurate but incomprehensible ones.

This phenomenon is especially evident in the following areas:

• Healthcare services
• Financial decision systems
• Legal and justice applications
• Defense systems
• Human resources processes

When decisions directly affect a person’s life, the answer “because the algorithm said so” is rarely considered sufficient.

The high accuracy rates of AI systems do not guarantee they will always make correct decisions. Biases in training data, incomplete information, or unexpected scenarios can lead to erroneous outcomes. For instance, a model trained on historical hiring data may inadvertently perpetuate past discriminatory practices. Similarly, insufficient representation of certain patient groups in health data can reduce diagnostic accuracy. When the decision mechanism is invisible, detecting these errors becomes significantly more difficult.

Explainable Artificial Intelligence refers to a set of methods designed to make the decision-making process of an AI system understandable to humans. XAI systems can answer questions such as:

• Why was this decision made?
• Which data inputs had the greatest influence?
• What factors could have changed the outcome?
• Which variables did the model weigh most heavily?
• What is the confidence level of the result?

For example, a credit evaluation system might explain that an application was rejected due to factors such as income level, current debt ratio, or payment history. This allows users not only to see the outcome but also to understand the path that led to it. ^【2】

In the past, explainability was often viewed as a desirable feature. Today, however, it is increasingly becoming a legal and ethical requirement across many sectors.

There are several key reasons for this:

When an AI system makes an error, who is responsible? If the reasoning behind a decision is unknown, assigning accountability becomes difficult.

Many countries are developing regulations requiring AI systems to be transparent. In applications that affect human rights, explainability is becoming increasingly important.

People place greater trust in systems that provide explanations. Transparency increases public acceptance of technology.

Explainable models make it easier to identify errors and biases within the system.

Advances in artificial intelligence are not limited to developing more powerful models. Equally important is making these models understandable to humans.

In the future, the most successful AI systems will not only deliver accurate results but will also clearly articulate the reasoning behind their decisions. Because trust is as critical as performance in the adoption of technology.

Explainable artificial intelligence stands out as one of the most important approaches addressing this need. People are not ready to leave decisions that affect their lives entirely to invisible algorithms. For AI to gain societal acceptance, transparency, explainability, and accountability are no longer luxuries—they are fundamental requirements. Explainable artificial intelligence stands out as one of the most important approaches addressing this need.