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.

Heat stroke poses a significant health risk, especially during extreme temperature conditions. While social media posts have demonstrated potential for detection of infectious diseases, its reliability remains a challenge. Now, researchers from Japan demonstrate the potential of combining social media posts and deep learning models for early detection of heat stroke risks. This approach opens up new possibilities for leveraging real-time data in event-based surveillance, enabling timely detection and response to heat stroke threats.

Researchers at HSE University and the London School of Hygiene and Tropical Medicine have identified 15 key motives that drive human behaviour. By analysing people's views, preferences, and actions through an evolutionary lens, they demonstrated how these motives intertwine to shape habits and interpersonal relationships. The findings have been published in Personality and Individual Differences.