How Increasing Diversity in STEM and AI May Address Bias in Generative Models

The rise of generative AI has highlighted the potential for bias in AI models, raising concerns about fairness and inclusivity as these technologies become more influential in critical areas like insurance, housing, credit, and welfare. Addressing this challenge may call for a more diverse workforce in AI and STEM fields.

Early education and exposure: Encouraging more women and minorities to pursue STEM careers starts with early education and exposure:

• Representation shapes perception, and subtle messages given to young girls can influence their interest in STEM fields.
• Equal paths for exploration and exposure should be ensured through regular curriculum and partnerships with non-profit organizations.
• Celebrating and amplifying women role models in STEM can inspire girls to see themselves in these careers.

Recognizing and mitigating bias: To effectively address bias in AI, it is crucial to acknowledge its existence and assume that all data and human judgments are inherently biased:

• Popular image generators have shown a lack of representation in body types, cultural features, and skin tones when depicting “beautiful women.”
• Ethnic dialect can lead to “covert bias” in AI models, influencing perceptions of intellect and even criminal sentencing recommendations.
• Historical gaps in women’s credit data or employment history due to legal or social factors may introduce bias if not properly accounted for in AI training.

The importance of diversity in AI development: A diverse representation of women must have an active voice in constructing, training, and overseeing AI models:

• Diverse perspectives are essential for identifying and addressing potential biases and unintended consequences.
• Leaving the development of AI models to a homogeneous group of technologists risks perpetuating biases and excluding important considerations.
• More accurate and inclusive AI models will benefit everyone, making increased diversity in STEM and AI talent a “no-brainer.”

Broader implications: While completely eliminating bias from AI innovation may be impossible, inaction or ignorance is unacceptable. Proactively addressing bias through increased diversity in STEM and AI development is crucial for ensuring fairness and inclusivity as these technologies become more influential in shaping our lives and livelihoods.