CONSTRUCTION OF PROBABILISTIC CAUSAL RELATIONSHIPS BETWEEN EQUIVALENCE CLASSES OF DATA IN AN INTELLIGENT INFORMATION SYSTEM - Chalyi, Serhii; Leshchynskyi, Volodymyr (Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, 2024)
The subject of this research is the processes involved in generating explanations for decision-making in artificial intelligence systems. Explanations in such systems enable the decision-making process to be transparent and comprehensible for the user, thereby increasing user trust in the obtained results. The aim of this work is to develop an approach for constructing a probabilistic causal explanation model that takes into account the equivalence classes of input, intermediate, and resulting data. Solving this problem creates conditions for building explanations in the form of causal relationships based on the available information about the properties of input data as well as the properties of the results obtained in the artificial intelligence system. To achieve this aim, the following tasks are addressed: developing a causal dependency model between the equivalence classes of input and output data; developing methods for constructing equivalence classes of data in the decision-making process and a method for constructing causal explanations. A probabilistic model of causal dependency is proposed, which includes a causal relationship between the equivalence classes of input or intermediate and resulting data obtained during the decision-making process in the artificial intelligence system. This relationship considers the estimates of the possibility and necessity of such a dependency. The model creates conditions for explaining the possible causes of the obtained decision. A set of methods for constructing equivalence classes of data in the decision-making process and for constructing causal explanations is proposed, establishing a causal relationship between the equivalence classes. When constructing equivalence classes, relations of mandatory and optional data refinement, requirements or exclusions of data, as well as data conjunctions, are established. When constructing causal explanations, the possibility and limitations of the necessity of such a dependency are calculated, allowing explanations to be built based on the available information about the obtained decisions and the input and intermediate data used to form these decisions.