Addressing the Gender Bias in Machine Learning

Despite growing awareness, the steps taken to mitigate gender bias in machine learning models are not sufficient. Bias in training data and algorithmic design often reflects societal inequalities, leading to models that perpetuate these issues. While there have been efforts to address this, such as enhancing dataset diversity and developing bias detection tools, these measures are not universally adopted. Stronger industry standards and regulatory frameworks are essential to ensure that machine learning promotes equity.