CAUSAL RELATIONS IN DATA ANALYSIS AND ARTIFICIAL INTELLIGENCE
Vladimir Moiseevich Reznikov
Institute of Philosophy and Law, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
Novosibirsk National Research State University, Novosibirsk, Russia
Keywords: the role of philosophy in science, mathematical statistics, verification of independence, randomization
Abstract
The article formulates arguments for the adequacy of philosophical concepts in the context of the use of data analysis and artificial intelligence. Firstly, we show the importance of teaching computer programs in the field of artificial intelligence about causal relationships to increase their adaptability, develop the ability to explain the results obtained and bring them closer to natural intelligence. Secondly, we show the heuristic nature of causal interpretation as a basis for the operational description of biasing factors and their rational blocking in randomized experiments. Finally, we describe the prerequisites for the hypothetical participation in science of philosophers who are proficient in formal methods as critics of their shortcomings and as consultants on the correct application of philosophical concepts in science.
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