Are we trusting AI too much? New study demands accountability in Artificial Intelligence
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Updates every hour. Last Updated: 16-Jul-2025 20:11 ET (17-Jul-2025 00:11 GMT/UTC)
Divorce can take a toll on children’s mental health, but new research from The University of Texas at Arlington reports that its effects may last far longer than expected, potentially increasing the risk of serious health issues decades later. According to findings by social work Associate Professor Philip Baiden recently published in the journal PLoS One, Americans aged 65 and older who experienced their parents divorcing as children were more likely to suffer a stroke compared to their peers—one in nine as compared to one in 15 whose parents did not divorce.
New research from Binghamton University, State University of New York reveals that adding periods to a text can make your message seem more intense.
New research by Adrian Ward, associate professor of marketing at Texas McCombs, validates the growing concerns about the ipsychological effects of cell phone use . In a controlled experiment, he found that just two weeks of blocking mobile internet from smartphones improved three dimensions of psychological functioning: mental health, subjective well-being, and attention span.
“Smartphones have drastically changed our lives and behaviors over the past 15 years, but our basic human psychology remains the same,” Ward says. “Our big question was, are we adapted to deal with constant connection to everything all the time? The data suggest that we are not.”
Tactile teaching materials are designed to make maths and science more accessible for people with a sight impairment. The EUniWell university alliance's Seed Funding Programme supports the development of these materials. A joint project of the EUniWell universities in Santiago de Compostela (Spain), Murcia (Spain), Florence (Italy) and Konstanz (Germany).
Researchers from the University of Navarra's Data Science and Artificial Intelligence Institute (DATAI) have developed a new AI framework to reduce bias in critical decision-making areas such as health, education, and recruitment. Their methodology optimizes machine learning models to ensure fairness by addressing inequalities related to race, gender, and socioeconomic status, among other possible algorithmic discriminations. Published in Machine Learning, the study combines conformal prediction techniques with evolutionary learning to achieve reliable and unbiased AI predictions. The researchers tested their approach on real-world datasets, demonstrating that it reduces discrimination without compromising accuracy. Their work provides policymakers and businesses with AI models that balance efficiency and fairness, aligning with ethical AI principles and legal requirements. The team has publicly made their code and data available to promote transparency and further research in responsible AI development.
Alzheimer’s disease (AD) — a neurodegenerative disorder — comes with a significant socioeconomic burden. Recent studies have found a strong association between AD and metabolic syndrome (MetS), a cluster of conditions that include diabetes, obesity, high blood pressure, and abnormal blood fat levels. In a recently published literature review article, researchers explore the link between AD and each individual component of MetS, analyzing the potential underlying mechanisms at cellular and molecular levels.