This Open Educational Resource (OER) addresses the gender data gap and AI bias. It explains how representative, algorithmic, cultural, and intersectional biases cause AI systems to disadvantage women, with concrete examples from hiring, health, finance, and media. The poster also highlights actionable strategies to close the gap, including inclusive datasets, debiasing methods, intersectional benchmarks, and transparency standards. Developed within the GEDIS project, the resource aims to support librarians, professors, and students in understanding and mitigating gendered biases in AI.