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  • Обыденные суждения о причинности и вине за непреднамеренные негативные последствия действий: экспериментальный подход к оценке влияния
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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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Обыденные суждения о причинности и вине за непреднамеренные негативные последствия действий: экспериментальный подход к оценке влияния

Гл. 7. С. 217–231.
Deviatko I. F., Gavrilov K. A.

The chapter raises the question of the specificity of causality and blame judgments in situations of unintended negative side effects of intentional actions. We focus on the naïve “theory of social action”, according to which there are limitations on the actor’s responsibility for intelligibly expected probable unintended damage of his actions. We propose that causality and blame judgments depend on (1) particular institutional domain of the action, (2) type of actor (individual, corporate or institutional actor), (3) type of inadvertent damage (property damage or health damage) and (4) “remoteness” of damage from its originator (immediate damage to an agent performing an action in a new situation created by the originator’s action either damage to a third party).

Language: Russian
Full text
Keywords: нормативное обыденное знаниеnormative lay knowledgecausality and blame judgmentsfactorial surveyinstitutional domaintype of social actorобыденные суждения о причинности и винефакторный экспериментинституциональная областьтип социального актора

In book

Обыденное и научное знание об обществе: взаимовлияния и реконфигурации
Обыденное и научное знание об обществе: взаимовлияния и реконфигурации
Deviatko I. F., Abramov R., Бруккмайер Карл, Gavrilov K. A., Kozhanov A., Зотов А. А., Катерный И. В., Подвойский Д. Г., Сапов В. В. М.: Прогресс-Традиция, 2015.
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