In What Cases Does Artificial Intelligence Often Make Mistakes: The Boundaries of Machine Learning Introduction: The Nature of AI Error as a Systematic Phenomenon Errors in modern artificial intelligence (AI) systems based on machine learning (ML) are not random failures, but regular consequences of their architecture, training method, and fundamental difference from human cognition. Unlike humans, AI does not "understand" the world semantically; it detects statistical correlations in data. Its errors arise where these correlations are disrupted, where abstract reasoning, common sense, or understanding of context is required. Analyzing these errors is critically important for evaluating the reliability of AI and determining the boundaries of its application. 1. The Problem of Data Bias and the "Garbage In, Garbage Out" Principle The most common and socially dangerous source of errors is bias in training data. AI absorbs and amplifies biases present in the data. Demographic bias: A well-known case with a face recognition system that showed significantly higher accuracy for light-skinned men than for dark-skinned women because it was trained on an unbalanced dataset. Here, AI did not "make a mistake," but accurately reproduced the imbalance of the real world, leading to an error in application in a diverse environment. Semantic bias: If the word "nurse" is more often associated with the pronoun "she" and "programmer" with "he" in the training data for a text model, the model will generate texts reproducing these gender stereotypes, even if the gender is not indicated in the query. This is an error at the level of social context that the model does not understand. Interesting fact: In computer science, the principle "Garbage In, Garbage Out" (GIGO) applies — "garbage in, garbage out". For AI, it has transformed into a more profound principle "Bias In, Bias Out" — "bias in, bias out". The system cannot overcome the limitations of the data on which it was trained. 2. Adv ... Read more