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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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Bias Perceptions of Generative AI: A Comparison of Public Sentiment on Twitter and Workplace Perspectives

Innovations. 2025. Vol. 80. No. 1. P. 281–304.
Uhunoma E., Maryann Asemota

Generative Artificial Intelligence (Gen AI) is one of the most
significant technological inventions in the last decade.It enhances content generation
across various domains, from personal messages to work-related tasks, encompassing
text, images, and videos. However, there have also been several debates surrounding
its inadvertent risks for bias and perpetuating stereotypes (Ferrara, 2023; Xavier 2024)
both from gender and racial perspectives (Nicoletti & Bass, 2023; Zhou, 2024;
Sadeghiani, 2024).Today, Gen AI is also being used in the workplace, with many
organisations adopting custom-built Gen AI tools as part of their working tools and
systems. Consequently, the objective of this research was tofind out if employees
perceive work-related outputs of Gen AI to be biased against women, people of colour
or neurodiverse people, and how this perception compares to that of the public, who
use Gen AI tools for both work and non-work-related purposes. A mixed-methods
approach was employed in this research. From workplace perspectives, quantitative
data were collected using a structured questionnaire from UK employees. From the
public perspective, qualitative data were collected through text mining using specific
keywords from Tweets on X platform. Findings showed that, while workplace
respondents reported modest levels of perceived bias across all groups, public
sentiment analysis and themes showed significant mistrust and negative perception of
bias in generative AI outputs for women and people of colour. There was however
positive perception as it relates to Neurodiverse people, with the public data showing
positive sentiments for Gen AI outputs as it relates to Neurodiverse people, as the users
view it as a tool for helping dyslexic users communicate better.

Research target: Computer Science Psychology Economics and Management
Language: English
Full text
Keywords: Social MediaSentiment AnalysisGenerative AIGen AIAI BiasWorkplace Perception
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