Machine Learning Ethics in the Context of Justice Intuition

• Main Authors: Mamedova Natalia, Urintsov Arkadiy, Komleva Nina, Staroverova Olga, Fedorov Boris
• Format: Article
• Language: English
• Published: EDP Sciences 2019-01-01
• Series: SHS Web of Conferences
• Online Access: https://www.shs-conferences.org/articles/shsconf/pdf/2019/10/shsconf_cildiah2019_00150.pdf

The article considers the ethics of machine learning in connection with such categories of social philosophy as justice, conviction, value. The ethics of machine learning is presented as a special case of a mathematical model of a dilemma – whether it corresponds to the “learning” algorithm of the intuition of justice or not. It has been established that the use of machine learning for decision making has a prospect only within the limits of the intuition of justice field based on fair algorithms. It is proposed to determine the effectiveness of the decision, considering the ethical component and given ethical restrictions. The cyclical nature of the relationship between the algorithmic algorithms subprocesses in machine learning and the stages of conducting mining analysis projects using the CRISP methodology has been established. The value of ethical constraints for each of the algorithmic processes has been determined. The provisions of the Theory of System Restriction are applied to find a way to measure the effect of ethical restrictions on the “learning” algorithm