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News and Events
STEM, Scully & Success: Empowering Women in STEAM
Why do girls leave STEAM fields and how can we close the gender gap by tackling stereotypes, boosting confidence, and creating inclusive environments?
Celebrating Hungarian Culture Day: Honoring Vilma Hugonnai
Dive into the inspiring story of Vilma Hugonnai. On Hungarian Culture Day, we celebrate this amazing woman who defied societal norms to become Hungary’s first female doctor. Her unwavering pursuit of her dreams paved the way for women in medicine and serves as a powerful reminder of the importance of perseverance in the face of adversity.
AI in the 21st Century: Breaking Stereotypes, or Reinforcing Them?
A recent UNESCO study highlights how many AI models are reinforcing, rather than challenging, gender stereotypes. The report reveals troubling gender biases in AI systems, impacting areas like hiring and voice assistants. Let’s explore the study’s key findings and discuss steps to address and correct these biases to promote more equitable and inclusive AI development.
The Gender Gap in AI: Where are women in AI
Despite rapid advancements, women remain vastly underrepresented in AI. Join us as we explore the critical importance of including women in AI development and examine how technology created without diverse perspectives can inadvertently perpetuate biases and fail to address the unique needs of women.
Bridging the Gap: Overcoming Obstacles to Gender Equality in STEM Education and R&I
This research report details a sociological study from the “STREAM IT” project, aimed at encouraging girls and young women to pursue education in STEAM fields. It provides a comprehensive overview of women’s status in STEAM across Europe and identifies critical points in girls’ educational paths for targeted interventions.
How AI Algorithms Can Reinforce Gender Bias—and What We Can Do About It
As we encourage diversity in science and technology, it’s crucial to address a hidden challenge: the gender biases embedded in AI systems. Dive with us in Genevieve Smith and Ishita Rustagi article, to discover how even advanced algorithms can inadvertently reinforce outdated stereotypes.