In fact a 2020 World Economic Forum report found that about a quarter of AI professionals were women. With this large-scale blockade comes the risk of backsliding.

"Some regulations, such as AI ethics, are in place, but we need to be more rigorous in reviewing the data we work on and checking that all groups are well represented in our datasets," says Arjeta Peshtani, a machine learning engineer.

Addressing the gender gap in AI therefore requires a multidimensional approach:

 Diversity in AI systems development teams in terms of gender, origin, culture, and personal experiences of the people working on those systems.

Data that reflects diversity: AI algorithms are trained on data. It is important to ensure that this data reflects diversity. This can be achieved by collecting data from a variety of sources and analyzing the data to detect biases.

 Transparency and accountability: Organizations developing AI systems must be transparent about the methods and algorithms they use and take responsibility for any resulting inequities and work to address them

Education and awareness: It is important to educate ourselves about the challenges and risks of gender inequality in AI. This can be done through educational programs, awareness campaigns and community discussions.

As we move into the age of AI it is necessary to address gender inequality with seriousness and determination.

Let's work together to create a future where Artificial Intelligence is not only powerful and intelligent but also fair and equal for all.

(The photo is a product of AI