How does an AI "make decisions"? A deep look at machine learning models

How AI Makes Decisions: A comprehensive and engaging look at machine learning models and how they work in the real world for the general public.

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June 10, 2025

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Artificial Intelligence and Decision Making: Why Does This Question Matter?

In today's world, where artificial intelligence is present in various aspects of our lives—from mobile phones to self-driving cars—understanding how an intelligent system makes decisions is of great importance. Many people think of AI as some kind of digital magic, but in reality, behind its decisions are mathematical logic, training data, and advanced machine learning models. In this article, we aim to explain this complex process to you in simple but precise language.

What are machine learning models and what role do they play in decision-making?

Machine Learning is a branch of artificial intelligence that allows computers to learn from data without direct programming. These models are a set of algorithms designed to find patterns in data. For example, if a model learns what features images of cats have, it can identify new images in the future that it hasn't seen before. In decision-making, the model makes a prediction or selection by analyzing the input (e.g., image, text, or audio) and comparing it with its prior knowledge.

Decision-making steps in a machine learning model

Decision-making in a machine learning-based system is done in stages. First, data is collected and analyzed by algorithms. Next, the model "learns" how to respond to similar inputs in the future using the training data. Finally, when new data enters the system, the model makes a decision using the knowledge it has previously acquired. This decision could be recognizing a face, recommending a product, or even answering your question in a chatbot.

How does an AI "make decisions"? A deep look at machine learning models

Are AI decisions trustworthy?

This question is one of the most important challenges in the world of artificial intelligence. Machine learning models can perform very accurately or completely wrong, depending on the quality of their training data. If the data is incomplete, biased, or incorrect, the decisions the model makes will also be unreliable. For this reason, in many sensitive industries, such as medicine or law, there must be transparency in AI decision-making and the ability to review it by humans.

The role of data in decision-making accuracy

Data is like "life experiences" for an AI model. The more data, the more accurate and diverse the data, the better decisions the model can make. If a model has only seen images of white cats, it may make a mistake in recognizing a black cat. Therefore, diverse and high-quality data is one of the main pillars of accurate decision-making in intelligent systems. Sometimes, data cleaning can even have a greater impact than designing complex models.

Real-world examples of AI decision-making

Today, many of the applications we deal with daily use machine learning models for decision-making. Movie recommendation algorithms on Netflix, facial recognition filters on Instagram, speech recognition systems in voice assistants like Siri or Google Assistant are all examples of this type of decision-making. Artificial intelligence has even been widely used in areas such as disease diagnosis or financial market analysis.

The Future of Decision-Making by Artificial Intelligence

In the future, as technology advances and the available data expands, AI models will be able to make even more complex decisions than they do today. However, ethical challenges, privacy, and transparency of algorithms must also be considered. Humans must remain at the center of the decision-making process to prevent discrimination or systemic errors. Combining human intelligence with artificial intelligence can create a smarter, more efficient, and more equitable future.