Only 29% of women use generative Ai at work, more training needed

Another problem to be solved is the low representation of women in the universe of developers: hence the risk of repeating or even amplifying gender biases that already exist. "In this field the workforce is just very low, 0.3% of global employment according to the OECD report. Women represent an extremely small group within this percentage, and having few women capable of designing computer systems and Ai carries the risk of perpetuating biases that sometimes we don't even realise we have".

Competitive Gap in the Use of Generative Ai

It is now a given that Ai will not replace people at work, but people who do not know how to use Ai are at risk of being replaced by those who are familiar with the new technology. The importance of acquiring technological skills will therefore be increasingly crucial in the future. That said, women are more at risk, as they use generative Ai less than their male colleagues. "This is a very worrying picture, given the impact these technologies will have on all professions. For the same jobs, women who say they use generative AI systems are 29% compared to 41% of men."

Women are in the group of jobs most 'disrupted' by Ai.

One can try to read this data, but the reasons why women make little use of generative A are multiple and sometimes ancestral. "It is well known that men," comments Irene Finocchi, "have a greater propensity to 'jump in', if there is a job position for which a certain number of requirements are requested, men try to apply even if they have half of them; women, if they do not have all or almost all of them, do not apply at all. Probably a similar mechanism also works in the approach to generative Ai, since women state that they do not feel adequately trained in the use of these technologies, do not feel confident enough and would like some training upstream. Moreover, there is another factor that might influence the lower propensity to use Ai. Women are employed more in a band of jobs that will be impacted and made obsolete by the new technology, while jobs in the managerial band, where men are the majority, will become more efficient and effective thanks to Ai. This is referred to as gap augmentation, and it stems from the fact that women work more in roles that will be most 'disrupted' by new technology, men work more in those that will be helped by Ai"

Working on training at all levels.

In order to give women the same opportunities, we must, of course, work on education, from schools to companies. In general, again due to prejudices and stereotypes in society, girls seem more attracted to more multidisciplinary degree courses than to pure engineering or computer science courses. 'Evidently,' explains the professor, 'for cultural reasons too, women seem less inclined to pursue courses with only technical subjects. When, when the facts prove that they also do very well in Stem subjects, they subsequently choose courses with more targeted addresses. Probably, therefore, girls start out discouraged and then, only when faced with the evidence of their success, become aware of their abilities in certain areas'.

Therefore, training projects that enhance the talent of girls are important, but at the same time such opportunities should not only be offered to female students, but also to students. Professor Finocchi cites as an example the project for which she is scientific referee at the Luiss Business School, called Ai Grow, which sees various companies proposing the resolution of business cases through the use of generative Ai tools to the entire student body of the specialised masters. "Collaboration is always a good thing, so it is good," he points out, "that students are encouraged, but also involving the students.

Moreover, the involvement of girls and young women must start from the early years of school. "We are working on a project that starts in middle school. Women girls must be encouraged to familiarise themselves with these tools right from the start, otherwise there will be a negative impact and they will fall behind'. In conclusion, bearing in mind Melvin Kranzberg's maxim that technology is neither good nor bad, but neither is it neutral, in order to prevent the gender gap from widening with technology and generative Ai, women need to be trained and inspired, right from the first approach in schools without neglecting the need for reskilling in companies.