There is a great opportunity to build generational wealth through employment in financial services professions like wealth management, mutual fund supervision and financial market trading. However, women are underrepresented in these professions, with some estimates indicating that women financial advisors are outnumbered by men at a ratio anywhere from 10:1 to 6:1.

Surveys of undergraduate women reveal complex socio-economic factors behind this disparity. Women perceive these fields as male-dominated, are less likely to consider them viable career paths compared to men, and often feel less confident about succeeding in the industry. They may also lack female role models, have limited financial literacy and struggle with self-limiting perceptions.

If how we consume information perpetuates this imbalance, we must look to generative AI to play a significant role in attracting, developing, and retaining women in financial services, ultimately reshaping the industry.

So, what is generative AI? Generative artificial intelligence (AI) empowers machines to create content such as text, images and even entire websites. This technology relies on neural networks and extensive datasets like census information, housing or financials to generate content that closely mimics human creations. A prime example of generative AI is the large language model (LLM) like GPT, GPT3.5, GPT4, BERT and others, widely used in natural language processing and content generation.

While generative AI holds immense potential, it also carries the risk of perpetuating bias. AI algorithms rely on publicly available data, and if these datasets are biased, the AI may inadvertently amplify these biases. For example, if training data predominantly features male pronouns and reinforces stereotypical male traits or professions, the AI can unintentionally reinforce gender stereotypes. A quick internet search of “he vs. she” revealed 25 billion instances of “he” and only 190 million of “she”: less than 1% of the former.

Creating unbiased AI tools is a complex and ongoing challenge, as biases can emerge from various sources including data used for training, the design of algorithms, and the human bias of developers themselves.

Stephanie Dalwin, research advisor at financial services consulting firm Datos Insights, emphasizes the importance of responsible AI usage. "AI, and particularly generative AI, needs to be wielded responsibly,” she said. “Unchecked, AI will reflect and intensify existing systemic inequities. Any AI-based recruiting tools in the financial services industry need to be designed equitably to reach more diverse talent pools and ultimately make our talent pipelines more inclusive."

The encouraging news is that there are best practices to ensure AI does not amplify systemic bias. Recruiters and HR professionals can identify potential bias by routinely conducting audits of their AI tools, utilizing various metrics and applications designed for this purpose. Non-profit organizations and educators can create gender-balanced educational AI tools by leveraging inclusive training data and diverse development teams.

Regardless of whether you are an AI expert or not, we all have a role to play in supporting the deployment of equitable AI. Before adopting a new tool, it is crucial to inquire about its underlying data sources, methodology, and the techniques employed for bias mitigation. This transparency not only sheds light on the products themselves, but also fosters a greater market demand for ethical and inclusive AI.

Developing unbiased AI tools transcends ethical considerations; it also carries profound social implications. Gender-balanced AI can attract more women to financial services professions by mitigating biases and fostering inclusivity. Additionally, it can cater better to the needs of female clients by providing personalized financial advice that considers diverse perspectives. Initiatives aimed at closing the gender gap should commence early; tailored financial education programs targeting young women from high school through college can substantially enhance gender balance. These programs should encompass various topics including financial literacy and investment advice, equipping women with both the knowledge and confidence to pursue careers in financial services.

AI can also serve as a valuable guide to career pathways within financial services, providing women with balanced perspectives on their opportunities for success in the industry. It can help them identify misinformation, understand cognitive biases and access more inclusive information.

Closing the gender gap is a complex challenge that demands a comprehensive approach. AI is not a single-use panacea and has to be used responsibly within the context of a multi-pronged approach. LLMs and AI will allow us to close the gap faster than initially thought possible because they can scale much larger than human effort alone. By harnessing unbiased AI tools, we can help women envision and achieve success—undeniably a world worth building toward.

Neeraja Rasmussen is the founder of Spyglaz, and the American College Center for Women Advisory Council member.