A Socio-Legal Perspective on Gender-Based Discrimination in Machine Learning Algorithm

The problem of gender-based prejudicialness began the moment women joined professional workplaces, businesses, and other institutions. Regardless of stringent regulations and laws, almost all organizations exhibit bias against women. However, its varieties and density may vary based on a country's location, industries, or level of prosperity. Due to this AI-ML algorithms are also getting affected producing biased results while recruiting women or at the time of promotion or appraisal because the data that ML is learning or getting trained is not equitable or fair. In this paper, we discussed those women in managerial roles face prejudicialness from their peers. This shows the problem's apex. Therefore, the prejudice against women must be addressed through coordinated efforts of every pertinent organization. Further in this paper, we identified the common algorithmic errors that cause biases and prejudicialness against women in the workplace. Thus, this paper shows the relationship between Artificial Intelligence, Machine Learning, and Gender Discrimination. In conclusion, the present paper proposes several gender discrimination mitigations through ML algorithms and legal techniques.