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News
July 24, 2026
'Physics Is What the World Is Literally Built On'
Physicist Nina Dzhanayeva, recipient of a Vladimir Potanin Foundation scholarship, focuses her research on nanophotonics. In this interview for the HSE Young Scientists project, she discusses nanowells, scientific intuition, and how physics can help in making frangipane cream puffs.
July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.

 

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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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