Despite ongoing conversations about gender diversity, women in tech still face underrepresentation, unequal pay, and discrimination and often find themselves outnumbered. Although strides have been made to foster diversity, yet the space for women to lead remains limited. However, companies prioritizing equity and inclusion, are making progress in promoting and retaining women in tech careers.

Algorithms utilize personal data that is collected pertaining to the situation, experience, or scenario they simulate. Data collection is crucial for machine learning and can introduce "bias" that could lead to "technological discrimination”. Nevertheless, algorithms cannot be held "guilty" as they are neither physical nor legal entities. The responsibility lies with individuals who make decisions regarding their use, implementation, and the resulting outcome.