"Gender bias in AI systems poses significant challenges to achieving equality. AI, dependent on data for training and decision-making, often perpetuates gendered inequalities due to biased datasets that reinforce stereo types and exclude diverse experiences. This impacts women's access to healthcare, employment and education, compounding structural inequities. Addressing these biases requires integrating gender-sensitive data throughout AI development to ensure fairness and equ ty. Barriers include technical opacity, binary gender norms and patriarchal structures, particularly in the Global South. Solutions involve algorithmic transparency, inclusive data frameworks, cross-sectoral collaboration and feminist ethics. Embedding gender data in AI is essential to empower marginalised groups and pro mote social justice..."

Support: Digital