جستجوی مقالات مرتبط با کلیدواژه « machine learning » در نشریات گروه « فلسفه و کلام »

The aim of this paper is to explore the ethical implications of artificial intelligence (AI) in the realm of art. Drawing on the philosophy and ethics of AI, this study examines the ethical challenges that may arise in the art domain. Given the increasing use and penetration of AI in the field of art, there is a need for more careful attention to the ethical dimensions of this phenomenon by scholars of art and philosophy. To achieve the objective of the paper, concepts such as AI, some of its techniques, and the ethical issues of AI in art are explained and analyzed using the analytical-descriptive method. The findings of the study reveal that ethical issues such as privacy and surveillance, manipulation of behavior, opacity and transparency, bias in decision systems, and automation and employment can be investigated in the field of art. These ethical issues are sometimes in conflict and sometimes in direct relation with each other. This study suggests that addressing the ethical questions of AI in the field of art requires addressing the ethical questions of AI in general.

One of the most important economic goals of countries is to provide financing from the securities market in order to prevent banking crises. Therefore, the impact of the base volume in the Iranian securities market to prevent speculation and in order to improve liquidity will not be possible except with the increasing link of economic ethics. The purpose of the current research is to investigate the consequences of reducing moral hazard on the application of the base volume and its relative costs on the liquidity of the securities of knowledge-based companies in the Tehran stock market.               

The method of the current research is practical and of the type of machine learning and the method of generalized moments. Two groups of small-medium stocks (a case study of knowledge-based companies) and large companies in the Tehran stock market were selected as the statistical population, which were selected from the ranks of the target population in 2014-2021 as the research sample. The data measurement tool was the Iran Stock Exchange website. The decision tree and torque method was also used for the test.

The fit of the research model, which included a main hypothesis, was confirmed. Therefore, the application of volume based on the market liquidity of two groups has a negative effect, which is more for knowledge-based companies and can cause moral hazards in pricing.

The Amihud index was used for estimation and the experimental results indicate that the volume of the base has a high importance in the lack of liquidity in the market, which is more important for knowledge-based companies and shows that it can cause moral hazards.

Deep learning technology, philosophical challenges and approaches Abstract The unprecedented human’s advancement in generating and storing piles of data, and exploiting such large amounts of data for building reasoning machines has manifested as a technology known as “deep learning”. This technology is inspired by the brain’s connectivity structure and is empowered by deep artificial neural networks. In spite of numerous benefits offered by their great power in reasoning like experts or creating things like skillful people, this technology imposes some ethical challenges to human’s life. This article tries to present the ethical challenges of deep learning technology that threaten humanity and tries to address them by employing a rational-philosophical approach. Although deep learning technology imposes several ethical challenges on our lives, it is still possible to benefit from big data without sacrificing our ethical values provided we gain awareness about and preparation against such challenges.