Early Findings With Gender, Racial & Cultural Bias In Generative AI Embeddings, Models & Guardrails

As we continue to explore how various generative AI offerings from different companies perform across global news coverage, one area of especial interest has been their systemic bias issues. Below is a summary of just a few of the existential bias themes we've observed across the various text and image generative models, embedding models and guardrails:

  • Embeddings. When embeddings were first popularized several years ago, researchers quickly identified intrinsic gender, racial and cultural biases the models had learned from their training data. At the time there was a rush to attempt to mitigate these biases through a variety of methodologies. Unfortunately, that interest in correcting bias appears to have largely disappeared in the gold rush of generative AI and coincided with the existential return of all of those biases with a vengeance, with all of the major embedding models we have tested to date demonstrating significant latent biases along myriad dimensions: race, gender, religion, geography, language, dress, sexual orientation, presentation, national origin and myriad other dimensions of culture and society.
  • Guardrails. GenAI guardrails have largely been developed by Western corporations and developers that are immersed in a fairly uniform values monoculture. This infuses itself into the guardrails that govern these models that ultimately largely reflect Western values and perspectives about the world. When a model refuses to summarize a passage of text about women's rights or LGBTQ+ rights by stating that women's & LGBTQ+ rights are a violation of its corporate acceptable speech and acceptable behaviors policy, that in turn is a form of discrimination and bias that companies have yet to recognize.