Artificial intelligence (AI) has the potential to significantly advance gender equality by improving social and economic outcomes. However, if not properly managed, AI can perpetuate or even create new gender inequalities. This publication by UN Women highlights the importance of gender-responsive AI. It emphasizes the need for inclusive, safe, trustworthy, and equitable AI systems, and outlines the role of the private sector in achieving this goal. It also calls for strengthening partnerships with the private sector to support women in AI leadership, foster opportunities for women entrepreneurs, bridge the gender digital divide, and ensure AI policies integrate gender dimensions.
Introduction
Artificial intelligence (AI) could significantly accelerate progress on gender equality. The rapid integration of AI into everyday life is creating opportunities to improve social and economic outcomes for all. However, a study by the United Nations launched in 2024 shows that the speed of development also brings challenges that could perpetuate or even create new gender inequalities if left unchecked.
The private sector, particularly the technology industry, is key to designing, developing and deploying inclusive, safe, trustworthy and equitable AI systems. While Member States are responsible for governing and regulating AI, the private sector has a pivotal role in developing responsible practices, such as removing gender biases in AI, promoting diversity and complying with legal standards. Recent positive action includes efforts to tackle biases in AI models and content, increase the number of women in the AI workforce and leadership positions, and implement safeguards against violence that may occur or be amplified by the use of AI technologies.
Yet, more needs to be done to maximize AI’s benefits for gender equality while mitigating risks and harms for women and girls. Priorities include addressing gender bias in AI systems, combatting the spread of misinformation and technology-facilitated gender-based violence, increasing gender-disaggregated data and integrating gender dimensions into AI policy and governance.