Concerns are rising regarding the potential for gender bias in artificial intelligence (AI) if more women aren't actively involved in its creation and data analysis. Experts warn that a lack of female representation in AI development could lead to biased platforms, impacting various sectors and potentially exacerbating existing inequalities.

Dr. Georgianna Shea, chief technologist at the Foundation for Defense of Democracies' Center on Cyber and Technology Innovation (CCTI), emphasizes that this issue extends beyond AI to the broader field of engineering. She stresses the importance of avoiding bias in any engineering process to ensure fair and representative outcomes.

The problem is twofold: The AI field needs more women to contribute to platform development, and the datasets used to train AI often contain pre-existing biases. Shea highlights the necessity of inclusive data collection, ensuring that information about women is adequately represented and considered.

She illustrates this with the example of nursing, a predominantly female profession. If AI primarily uses data from female nurses, it might generate conclusions that disadvantage male nurses seeking information through the platform. Shea further explains that physiological differences between men and women can influence data outcomes, potentially leading to skewed results if these differences aren't accounted for during testing and development.

The issue of gender bias in AI has been a topic of discussion for years. The Stanford Social Innovation Review highlighted potential problems in 2019, noting that processing data without considering gender can obscure important differences and lead to misrepresentation. This concern is further underscored by the underrepresentation of women in the tech industry, comprising only about 28% of the workforce as of 2022.

Shea draws a parallel to the historical design of military equipment, which was initially tailored solely for men. Only recently, with the full integration of women into combat roles, has the military begun adapting equipment to accommodate diverse physical characteristics. She emphasizes that the key to avoiding similar biases in AI is to consider the context of its application, understanding its purpose, potential impact, and the necessary safeguards against incorporating societal and data biases. This involves identifying and addressing potential biases during the development process and ensuring that gender is not a factor in selection criteria.