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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Искусственный интеллект и социальное неравенство: философско-правовой аспект

Теоретическая и прикладная юриспруденция. 2024. № 4 (22). С. 56–67.
Рыбин А. И., Chaschukhin Y.

The purpose of this article is to analyze the impact of artificial intelligence (hereinafter also referred to as AI) on social inequality. It is evident that AI not only brings about advantages but also serves as a means of infringing upon human rights, exacerbating social stratification at both the level of individual societies and on a global scale. Through the application of formal legal and comparative legal methodologies, it becomes apparent that the current practices of utilizing AI in various legal domains often fall short of achieving the intended objectives, sometimes even serving as a catalyst for discrimination and the perpetuation of social inequality. The paper underscores the active engagement of scholars and practitioners in addressing social inequality and other challenges associated with AI through the lens of AI ethics. The concept of AI ethics is explored, along with a critical analysis of ethical frameworks adopted by several technology companies operating in the field of artificial intelligence. It has been demonstrated that the primary cause of the emergence of social disparity in the realm of AI is not a dearth of ethical tenets in AI-driven algorithms, but rather a lack of adequate formalization of these ethical principles themselves into machine-readable language. It has been shown that the legal regulations in this domain, typically, are advisory in nature and emanate from corporations rather than from state institutions.

Language: Russian
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Keywords: этикасоциальная стратификацияethicsцифровое неравенствоdigital inequality social stratification artificial intelligence искусственный интеллектMarxist critique of inequalityмарксистская критика неравенства
Publication based on the results of:
Legal mechanisms of overcoming inequality (2024)
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