Issue |
SHS Web Conf.
Volume 69, 2019
The International Scientific and Practical Conference “Current Issues of Linguistics and Didactics: The Interdisciplinary Approach in Humanities and Social Sciences” (CILDIAH-2019)
|
|
---|---|---|
Article Number | 00150 | |
Number of page(s) | 7 | |
DOI | https://doi.org/10.1051/shsconf/20196900150 | |
Published online | 25 October 2019 |
Machine Learning Ethics in the Context of Justice Intuition
Plekhanov Russian University of Economics,
36, Stremyanny lane,
117997,
Moscow,
Russia
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
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.