In a time when technology dominates almost all aspects of human life, artificial intelligence (AI) and machine learning are becoming key factors of change. However, with the rapid development of these technologies, a challenge arises in understanding how they work, which often results in mistrust and uncertainty among users. Transparency and understanding of these systems are becoming imperative.

Artificial Intelligence in Everyday Life

Today, AI is used in many everyday situations. From recommendations on streaming platforms, to personalized ads on the internet, to smart assistants in households like virtual assistants. AI helps people in many ways, but its invisible nature often leaves users unaware of how these decisions are made.

Criticism of the Black Box

The "black box" concept is often used to describe machine learning algorithms. This means that users, and even developers, often do not know exactly how the algorithm arrives at certain results. Such opacity can be dangerous, especially in sensitive areas like healthcare, finance, or justice.

Explainable Models

To address this problem, scientists and researchers are developing explainable AI models. The goal is for users to receive clear and understandable explanations of how decisions are made. For example, instead of simply issuing a credit recommendation, an explainable model could show factors such as credit history, income, or debt that influenced the decision.

Practical Applications in Medicine

One of the most exciting examples of AI application is in medicine. Artificial intelligence systems today assist doctors in diagnosing, analyzing medical images, and predicting risks for certain diseases. However, in order to increase patient trust, it is crucial to explain how AI systems arrived at these conclusions.

Ethics and Artificial Intelligence

Along with technical challenges, there are also ethical issues. How can we ensure that algorithms are fair and unbiased? What if the system makes a decision that negatively affects an individual? These questions open important debates about the responsibility and regulation of AI systems.

Transparency as a Solution

Transparency is emerging as a key step towards greater trust in AI. Users must have the ability to understand the decisions made by systems and have access to information about how these systems work. Only in this way can a balance between technological progress and user trust be achieved.