Did we manage to solve the gender bias in AI yet?

Gender bias in AI remains an unsolved and growing problem. AI systems trained on real-world data inherit and amplify societal biases, affecting hiring, credit scoring, healthcare, and workplace automation risks. Bias can enter at any stage — data collection, algorithm design, deployment, or feedback loops — and word embeddings visually demonstrate how occupational and social stereotypes are encoded in language models. Women make up only 22% of AI professionals and are overrepresented in jobs most vulnerable to automation. While global institutions and governments are increasingly aware and discussing regulation, gender parity is still estimated to be 123 years away. Solutions require better training data quality, greater diversity in AI development teams, and mandatory bias audits — alongside broader societal change.