Gender bias in IT: Cracking the code

In this article, we describe how gender bias is present not only in the use used in our daily lives, but also in machine learning, which has an impact on technologies. To reverse the gender bias, teams must have gender diversity, and algorithms cannot be considered biased. To this research paper, we run a systematic literature review of articles, reports and websites, to understand the associated concepts of gender inequality, and how society is fighting gender inequality. An initial selection of keywords was made to be the core of this article. Subsequently, the selection of information sources that contained information within the scope of these concepts was carried out. Research shows that progress has been made in giving men and women equal opportunities in IT roles. Although data points to a very low percentage of women in digital when compared to men there are commitments made and incentives for it to "be for women" as well as for men in the future.