Regulating Algorithmic Bias and Global Legal Approaches in Gender, Diversity, and Intersectional Equity in AI-Driven Professional Systems

Algorithmic discrimination takes many shapes, each amplifying bias in AI-powered career platforms. AI tools used for hiring, promotions, performance ranking and workplace monitoring can inscribe and amplify instead of erasing gender and racial inequality. There is a potential for bias due to unrepresentative training data, the use of proxy variables rather than protected characteristics in decision making, design trade-offs that favour system efficiency over equity and feedback loops that keep into the data “successful” profiles from minority groups. When researchers have looked more closely at AI hiring systems, they have seen intricate interactions among multiple categories, where the model might favor some women overall while being particularly biased against certain groups of Black men or particular communities of women from a given ethnic background an insight that reveals discrimination to be not something that emerges cleanly along individual axes like gender and race.